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• Published: 08 February 2022

Banknote authenticity is signalled by rapid neural responses

• Daniel B. Dodgson 1 &
• Jane E. Raymond 1  

Scientific Reports volume  12 , Article number:  2076 ( 2022 ) Cite this article

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Authenticating valuable objects is widely assumed to involve protracted scrutiny for detection of reproduction flaws. Yet, accurate authentication of banknotes is possible within one second of viewing, suggesting that rapid neural processes may underpin counterfeit detection. To investigate, we measured event-related brain potentials (ERPs) in response to briefly viewed genuine or forensically recovered counterfeit banknotes presented in a visual oddball counterfeit detection task. Three ERP components, P1, P3, and extended P3, were assessed for each combination of banknote type (genuine, counterfeit) and overt response (“real”, “fake”). P1 amplitude was greater for oddballs, demonstrating that the initial feedforward sweep of visual processing yields the essential information for differentiating genuine from counterfeit. A similar oddball effect was found for P3. The magnitude of this P3 effect was positively correlated with behavioural counterfeit sensitivity, although the corresponding correlation for P1 was not. For the extended P3, amplitude was greatest for correctly detected counterfeits and similarly small for missed counterfeits, incorrectly and correctly categorised genuine banknotes. These results show that authentication of complex stimuli involves a cascade of neural processes that unfolds in under a second, beginning with a very rapid sensory analysis, followed by a later decision stage requiring higher level processing.

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Counterfeit refers to objects that closely resemble well-recognised, valued objects but are nevertheless different in subtle ways. Not only is the discrimination of counterfeit objects from their genuine counterparts critical for food (plant or prey) and predator identification for many animal species 1 , it is increasing important for humans due to the rapid increase of counterfeit goods in contemporary marketplaces 2 , 3 and use of faked images on social media 4 . In these human scenarios, counterfeit are unauthorised reproductions of recognised products that have been manufactured with the specific intention to deceive others. Discriminating counterfeit from genuine objects is a demanding perceptual and cognitive task that pits rapid, low-level sensory analysis against slower, high-level object recognition mechanisms. On the one hand, cues to counterfeit (‘tells’) are typically subtle variations of one or more sensory features, e.g., altered hue or distortion of an object part. Yet, the brain’s high-level object recognition mechanisms routinely discount many such subtle sensory variations as they are usually irrelevant to object classification 5 . For example, a chair or even a particular chair is easily recognised when viewed from different viewpoints or under different sources of illumination that may subtly change the chair’s hue. Even though the retinal image of the chair may be substantially different under different viewing conditions, its recognition remains robust, a phenomenon known as object constancy (for a review see 6 ). Indeed, high-level object recognition is thought to involve accumulating just enough sensory information 7 to allow a match between incoming information and stored internally represented object protypes or exemplar sets 8 . To gain efficiency, this matching mechanism depends on selecting only highly relevant sensory information that is diagnostic of object category, ignoring sensory variations that reflect situational factors, such as lighting, object orientation, observer viewpoint, or the presence of occluding objects or shadows 9 . Although this process allows rapid, flexible object recognition, it impedes counterfeit detection as the latter depends largely on detection of subtle sensory cues.

High-level object recognition is widely assumed to involve predictive coding 10 , 11 . In this view, the brain anticipates the appearance of an object based on learned and contextual factors and sets information processing mechanisms to selectively boost sensitivity to relevant sensory cues and reduce sensitivity to non-relevant cues. This may be achieved by a series of feedback loops whereby high-level cortical networks exert object-specific top-down modulatory effects that act on initial, incoming sensory signals about 100 ms after stimulus onset 12 . Such processes depend on experience and indeed, expertise in recognizing specific types of complex visual objects is associated with the appearance of early ERP components that are not found in novices 13 . Although the incoming ‘forward sweep’ of sensory information may be different for counterfeit versus genuine objects and could thus provide a useful signal to the brain, differences could be obscured subsequently if top-down object-oriented filtering were imposed. However, if incoming signals were sufficiently unexpected, they could alert high-level strategic mechanisms to alter or widen object recognition processes 9 , prolonging the high-level analysis needed for object categorisation and thereby facilitating counterfeit detection. This notion of a two-stage process in counterfeit detection that begins with sensory cue detection, followed by greater cognitive engagement of object analysis mechanisms has been previously proposed to account for behaviour 14 and eye movements 15 made during a banknote authentication task. However, direct evidence of the underlying neural mechanisms that could mediate such a two-stage process is lacking.

To probe the underlying neural processes involved in object authentication, we measured how sensory signals that differentiate counterfeit from genuine are propagated through the brain using electrophysiology combined with a banknote authentication task. We studied banknote authentication as banknotes provide complex, yet familiar stimuli and the task has practical everyday implications. We recorded event related potentials (ERPs) with scalp electrodes and presented stimuli using an oddball paradigm, a procedure widely used to study stimulus categorisation 16 , 17 , 18 . We chose this paradigm because it mimics the real-world conditions wherein encountering a counterfeit is rare and encountering genuine notes is frequent. In this procedure ERPs produced in response to standard (frequent) stimuli are compared to those produced in response to oddball (infrequent) stimuli when presented in an intermixed, non-predictable series. Here, genuine banknotes served as standards and convincing, forensically recovered counterfeits served as oddballs. Participants judged each note as real or fake on each presentation.

Specifically, we focussed on three ERP components: the P1, the P3, and the extended P3 (sometimes referred to as the “prolonged P3” or “slow wave”) 6 . P1, the first positive-going wave, is observed over occipital electrodes about 100 ms post-stimulus onset and is driven largely by the physical characteristics of stimuli, thus reflecting the forward sweep 19 . We predicted differences in this component for genuine (standard) versus counterfeit (oddball) banknotes, regardless of the participant’s eventual overt authenticity judgement. The second ERP component, P3, is the third positive-going ERP peak typically observed around 300–650 ms post stimulus onset and is maximal at central and parietal electrode sites. Unlike P1, P3 is influenced endogenously, being greater for unexpected (oddball) than expected (standard) stimuli 16 , 20 , and is presumed to occur during or after stimulus categorisation and probably before response selection 17 . The P3 is also sensitive to the difficulty of the target to nontarget discrimination 18 , exhibiting longer latencies for more difficult stimulus categorisations 21 . Here, we predicted P3 oddball effects to be evident and to be correlated with the accuracy of overt authenticity judgements.

Previous studies show that when stimuli are especially difficult to discriminate, as in our study, P3 oddball effects are prolonged beyond the typical P3 time window 6 , 22 , 23 . An extended P3 has been interpreted as evidence of the onset of a second level of cognitive engagement that commences after the initial feature matching categorisation thought to invoke the primary P3 6 , 24 , 25 , 26 . We therefore predicted the oddball effect to be observable during the extended P3 time interval, especially for correctly detected counterfeit. P3 and the extended P3 have been closely linked to conscious stimulus categorisation suggesting that these components could depend on overt authentication decisions 27 . To investigate, we analysed these components separately for trials resulting in each possible response (“real”, “fake”) for each note type (genuine, counterfeit). “Fake” responses to genuine notes reflect either guesses or decisions made without sensory counterfeit tell signals; “real” responses to counterfeits could also reflect guesses but they may also indicate that an available sensory tell signal was overridden by a later categorisation process. We predicted that only when counterfeits were correctly and consciously reported would prolonged P3 effects be observed, as these are thought to reflect sustained attention and working memory updating used for complex processing 25 , 26 . Such high-level, conscious processing of bone fide counterfeits (i.e., supported by sensory evidence) would be highly task relevant and beneficial, and therefore should be associated with a large extended P3.

In addition to the univariate ERP analysis, multivariate pattern analysis (MVPA) can be used to decode differences in the mental representations 28 associated with genuine and counterfeit banknotes, and between “real” and “fake” responses to authentic and counterfeit banknote across all EEG channels. Here, MVPA was used to corroborate the ERP approach and to investigate the time course of judgements for counterfeit and genuine banknotes. Specifically, MVPA was used to assess temporal generalizations that indicate the stability of genuine and counterfeit representations across time 29 . We predicted that the time window for accurate decoding of genuine and counterfeit banknotes would mirror that of the P3 component of the ERP because of the high-level processing required to differentiate the stimuli. We further predicted that stable representations, and therefore accurate decoding of responses to counterfeit banknotes would be protracted compared to the response to genuine banknotes. This is expected because identification of counterfeit flaws should involve secondary processing, sustained attention, and working memory. These predicted patterns of univariate and multivariate electrophysiological response would not only provide insight into authentication processes in a wide range of contexts but also inform object categorisation processes more generally.

Twenty-three British adults were presented with a series of brief (300 ms) UK banknotes image presentations (576 of genuine and 144 of counterfeit; half being £20; remaining, £50). Note type (genuine, counterfeit) was fully crossed with denomination; notes for each denomination were presented in separate blocks. Each note was presented upright and front facing in a short movie clip. During each clip, the note rotated slightly around its y-axis, emphasising the visual characteristics of the primary security features. The principal measures were overt counterfeit sensitivity (d′) and ERP components; each was analysed for effects of denomination and note type.

Behavioural data

As viewing time was brief , counterfeit sensitivity was relatively low (mean £20 d′ = 0.95 (s.d. = 0.83), mean £50 d′ = 0.94 (s.d. = 0.97), and similar for each banknote denomination ( t (1, 22) = 0.001, p  = .981). Response times (RT) were slower for counterfeit (mean = 905 ms, s.d. = 206) than for genuine notes (mean = 823, s.d. = 206; t (22) = 2.739, p  = .012), an effect that may have been partly due to use of the left hand for "fake” responses, but did not vary significantly with denomination ( F (1,22) = 2.315, p  = .142, ηp 2  = 0.095). RT effects of banknote type did not interact significantly with denomination ( F (2, 44) = 2.024, p  = .144, ηp 2  = 0.084).

P1 component at electrode location Oz

ERPs obtained for each note condition are shown in Fig.  1 a. For both the £20 and £50 notes, mean amplitude of the P1 component of the ERP was significantly larger for counterfeit (dashed lines; £20 = 0.924 µV, £50 = 0.183 µV) versus genuine (solid lines; £20 = − 0.177 µV, £50 = − 0.502 µV) notes ( F (1, 22) = 10.085, p  = .004, ηp 2  = 0.314), showing a clear oddball effect. Across authenticities, P1 mean amplitude was also significantly larger for £20 (0.373 µV) compared to £50 (0.339 µV) notes ( F (1, 22) = 6.389, p  = .019, ηp 2  = 0.225) as shown in Fig.  1 b. The magnitude of the P1 difference between counterfeit and genuine notes (oddball effect) was not significantly different for £20 and £50 notes ( F (1, 22) = 2.116, p  = .160, ηp 2  = 0.088). The correlation between P1 mean difference scores and d′ was low and non-significant ( r  = 0.249, n = 23, p  = .251).

( a ) Group mean amplitude (µV) of the ERP for each denomination and authenticity at electrode location Oz (where P1 was measured) plotted as a function of time relative to banknote presentation onset (0 ms). Blue and red lines represent £20 and £50 note conditions, respectively. Solid and dashed lines represent genuine and counterfeit banknote conditions, respectively. Positive is plotted up. The shaded rectangle indicates the time interval for P1. ( b ) Group mean amplitude at the P1 component plotted for each authenticity and denomination (including the average of each denomination). Error bars represent ± 1 within-subject S.E of the mean 30 .

P3 component at electrode location Pz

See Fig.  2 a. For both the £20 and £50 notes, the P3 component of the ERP was significantly larger for counterfeit (£20 = 9.93 µV, £50 = 10.39 µV) versus genuine (£20 = 8.51 µV, £50 = 7.61 µV) notes ( F (1, 22) = 19.543, p  < .001, ηp 2  = 0.470; £20: t (22) = 2.337, p  = .029, £50: t (22) = 4.224, p  < .001) as shown in Fig.  2 b. There was no difference in the magnitude of the P3 oddball effect (counterfeit v. genuine) for £20 and £50 notes ( t (22) = 1.622, p  = .119).

( a ) ERP for each denomination and authenticity at electrode location Pz, where the P3 component mean amplitude within the 450–600 ms window is calculated. Blue lines represent £20 notes, red lines represent £50 notes. Solid lines are genuine notes, dashed lines are counterfeit notes. Positive is plotted up. Shaded rectangle indicates the general time interval for each component. Group mean amplitude at the P3 ( b ) and the extended P3 ( c ) components plotted for each authenticity and denomination (including the average of each denomination). Error bars represent ± 1 within-subject S.E of the mean 30 .

The P3 oddball effect correlated strongly with d′ ( r  = 0.629, p  = .001) as shown in Fig.  3 a. Figure  3 b shows that there was no correlation between the oddball effects observed in the P1 and P3 components.

( a ) Mean counterfeit sensitivity (d′) collapsed across all denominations and counterfeit qualities as a function of the P3 oddball effect (counterfeit—genuine) at Pz. ( b ) P3 (450–600 ms) oddball effect at electrode Pz as a function of P1 (50–100 ms) oddball effect at electrode Oz. Each dot represents data from one participant.

Extended P3 component at electrode location Pz

As can be seen in Fig.  2 a, the difference in the waveforms for genuine and counterfeit notes was not limited to the traditional P3 time window (450–600 ms). Here, we observed large differences in the ERPs associated with genuine versus counterfeit notes until around 900 ms after the onset of the banknote’s presentation. Indeed, the mean amplitude of the extended P3 was significantly ( t (22) = 6.133, p  < .001) higher for counterfeit (mean = 6.89 µV) versus genuine (mean = 4.76 µV) notes as shown in Fig.  2 c. Interestingly, this extended P3 effect did not correlate with behavioural counterfeit sensitivity (d′, r = 0.255, n = 23, p  = .240), suggesting that this signal reflects the believed authenticity rather than the actual authenticity of the notes.

Response-based analysis

P1 and P3 components. If brain states drive behaviour, then ERPs might be expected to differ based on overt behavioural response rather than on the sensory information available. To investigate, ERPs at Oz and Pz were computed separately depending on response (“real”, “fake”) for each physical note condition (genuine, counterfeit), collapsed across denominations. See Fig.  4 a.

ERPs measured at two different electrode sites for each genuine (solid lines) and counterfeit (dashed lines) notes when the response was correct (green) and incorrect (black). Positive is plotted up. Shaded rectangle indicates the general time interval for each component. ( a ) ERPs obtained from electrode site Oz where effects on the P1 were observed. ( b ) Group mean amplitude at the P1 component plotted for each authenticity and the participants’ response. ( c ) Potentials obtained from electrode site Pz where effects on the P3 and extended P3 components (around 500 ms) were observed. Group mean amplitude at the P3 ( d ) and the extended P3 ( e ) components plotted for each authenticity and the participants’ response. Error bars represent ± 1 within-subject S.E of the mean 30 .

Analyses showed that the P1 component (Fig.  4 a) was unaffected by participant’s response ( F (1, 18) = 0.318, p  = .580, ηp 2  = 0.017) and there was no interaction between authenticity x response ( F (1, 18) = 0.503, p  = .487, ηp 2  = 0.027) (Fig.  4 b). This finding suggest that this very early component may underpin the initial accumulation of evidence needed to evoke a sense of suspicion towards potentially counterfeit notes. A similar picture holds for the P3 component (Fig.  4 c, d). It was unaffected by response category ( F (1,18) = 1.639, p  = .217, ηp 2  = 0.083), and showed a non-significant interaction between note authenticity and response ( F (1, 18) = 1.764, p  = .201, ηp 2  = 0.089).

Importantly, differences between the genuine and counterfeit note conditions for P1 and P3 components remained significant even when analyses were confined to trials resulting in a “real” response. Specifically, when ERPs generated in response to genuine notes that resulted in “real” (correct) responses were compared to ERPs generated in response to counterfeit notes that also resulted in a “real” (incorrect) response, both the P1 ( t (18) = 3.101, p  = .006) and P3 ( t (18) = 2.695, p  = .015) were significantly different. This indicates that from approximately 50–500 ms after onset of the banknotes image, neural information accurately coding physical authenticity of the banknote was available in the brain but failed to influence the overt behavioural decision appropriately.

Response-based analysis: extended P3 component

See Fig.  4 c. In marked contrast to the effects found for P1 and P3 components, analysis of extended P3 amplitudes showed that ERPs generated in response to counterfeit notes and accompanied by a correct “fake” response were markedly greater ( F (1, 18) = 69.562, p  < .001, ηp 2  = 0.487) compared to all other note-response combinations (all t s > 4.531, all p s < 0.001) as shown in Fig.  4 e. This extended P3 component likely reflects conscious detection of the oddball (counterfeit) after many sources of information (e.g., expectation) have been combined. However, it is unlikely to reflect the decision outcome itself because counterfeit judged to be fake and genuine banknotes also judged to be fake (error) produced different extended P3 amplitudes. (Compare the green dotted line with black solid line in Fig.  4 c.).

Classification accuracy data

Genuine versus counterfeit banknotes.

See Fig.  5 a and d. The authenticity of the banknote could be predicted significantly ( p  < .05) above chance as a stable representation between 450 and 900 ms following the presentation of the banknote, an interval that corresponds to the timing of the P3 component of the ERP and likely reflects the dominance of this component across the scalp in the EEG.

The top panels show the classification accuracy across time and the bottom panels show the temporal generalization matrices between ( a and d ) genuine and counterfeit banknotes; ( b and e ) “real” and “fake” responses to genuine banknotes; and ( c and f ) “real” and “fake” responses to counterfeit banknotes.

Response-based analysis: genuine and counterfeit banknotes

When the banknote was genuine (Fig.  5 b, e), the response (“real” or “fake”) could be predicted significantly better than chance between 380 and 1000 ms following the presentation of the banknote. However, when the banknote was counterfeit (Fig.  5 c, f), the response could not be predicted significantly better than chance until considerably later than for genuine banknotes. For counterfeit banknotes, predictions of the response were significantly better than chance from 680 to 800 ms following the onset of the banknote, a finding that suggests decisions about the authenticity of counterfeit banknotes take longer than that for genuine banknotes.

Authentication involves a combination of sensory discrimination and higher-level object categorisation to determine if a valued object is genuine or counterfeit. Here, for the first time, we monitored human electrophysiological responses as people authenticated briefly presented genuine banknotes and forensically recovered counterfeit. Banknotes were presented in a visual oddball paradigm involving genuine banknotes as standards and counterfeit as oddballs. ERPs, specifically P1, P3, and the extended P3, to standard stimuli (genuine) or oddballs (counterfeit) were assessed and compared so that the neural signature differentiating genuine from counterfeit, i.e., the authentication signal, could be tracked temporally after stimulus onset. We then linked the magnitude of the authentication signal with performance to determine the utility of its information for the behavioural task. Lastly, we compared ERPs for each note (genuine, counterfeit) and response type (“fake”, “real”) to examine whether variations in component amplitude reflected stimulus type, overt response, or a combination. Results reveal that a cascading series of neural signals unfolds during authentication of a counterfeit note. Very quickly after viewing the banknote, sensory information that differentiates counterfeit from genuine is coded and then top-down neural processes, e.g., expectation, appear to modulate how this early sensory signal contributes to overt authentication judgements. Lastly, authentication decisions for genuine banknotes are made more quickly than those regarding counterfeit banknotes. When the latter are identified, protracted high-level engagement appears to be evident, a process that may support category learning.

To reduce the likelihood that effects found here were limited to a particular banknote design or its specific counterfeit, two different banknotes (UK £20 and UK £50) were examined, creating a replication within the study. No differences due to denomination were uncovered even though the two notes and their counterfeits differed in colour, size, and authentication-relevant design features.

The authentication task used here was difficult, attention-demanding, and performance averaged only 65% correct, (with a group average d′ = 0.94, s.d. = 0.86). Nevertheless, P1 responses provide clear evidence that the sensory information needed to differentiate counterfeit from genuine was available to the brain within 100 ms after stimulus presentation. P1 oddball responses were significantly greater than P1 standard responses, an effect that is widely considered to reflect purely sensory processes with little or no top-down or attentional influence 19 . Our novel finding of a P1 oddball effect in the context of authentication shows that the brain can rapidly and automatically register subtle sensory differences between genuine and counterfeit, a finding consistent with other observations that P1 amplitude is extremely sensitive to the physical characteristics of stimuli 31 . Interestingly, these early pre-conscious brain signals are insufficient to support overt authentication behaviour as the magnitude of an individual’s P1 effects did not correlate with their counterfeit sensitivity performance (d′). However, as these early signals reflect a mismatch between the expected standard and current sensory data of a counterfeit, they may serve to trigger or direct high-level, selective attention to specific scene elements so that appropriate overt behaviour response, e.g., banknote rejection, is more likely to occur.

Evidence to support this notion was found in the oddball effects for P3. P3 amplitude is widely viewed as an index of post-sensory processes, including stimulus categorisation 25 , working memory 32 , conscious perception 33 , and conscious recognition of the decision category 27 , 34 . As with P1, P3 responses measured here were greater for counterfeit versus genuine banknotes indicating that the P3 oddball effect also indexes available sensory differences. Here, the P3 was observed relatively late, between 450 and 600 ms post-stimulus onset, probably reflecting the difficulty of the discrimination task 25 and possibly the modest temporal variability in the availability of cues in the slowly rotating banknotes. When compared to the P1 oddball response, however, the P3 authentication signal appears to code information with critical task relevance. This probably reflects active selective attention of specific information within the banknote. The magnitude of the P3 oddball effect correlates strongly with counterfeit sensitivity (d′) whereas its P1 counterpart does not. In other words, finding that the P3 oddball effect is predictive of performance is consistent with the conjecture that top-down attention processes influence neural activity during the interval between 450 and 600 ms post-stimulus, but not earlier. Additional support for this view is that MVPA classification of the banknote authenticity was not accurate until the P3 time window. The lack of a stable mental representation prior to the P3 time window suggests that without support from top-down processes, bottom-up signals are insufficient to decipher the authenticity of the banknotes. Furthermore, the P1 oddball effect did not correlate with the P3 oddball effect.

Although the P3 data reported here are consistent with views that P3 indexes post-sensory, conscious category recognition, data obtained thereafter (extended P3; 600–900 ms) appear to reflect an elaboration of categorical processing and working memory. Indeed, further processing after P3 is generally associated with tasks requiring complex categorical decisions 6 , as required here. Although P1 and P3 magnitude for each stimulus type (genuine, counterfeit) did not differ depending on response (“fake”, “real”), extended P3 amplitude was determined by an interaction of stimulus type and response. Specifically, correctly detected counterfeit produced a greater extended P3 than all other combinations. This means that a large extended P3 did not merely reflect a decision to report a banknote as fake, as it was larger when counterfeit was classed as ‘fake’ than when genuine notes were classed as ‘fake’. Moreover, it also cannot reflect a purely stimulus driven response, as it was greater for correct than incorrect counterfeit trials, even though the stimuli were similar in both cases. The pattern of extended P3 effects found here suggests that this component indexes confident counterfeit decisions that had been supported by sensory authentication signals extracted earlier in processing, rather than counterfeit judgements based on guessing or expectation.

The extended P3 has previously been attributed to the instigation of a secondary cognitive tasks that occur after initial stimulus categorisation 24 . In the present context extended P3 effects may reflect additional, confirmatory, internal elaboration of counterfeit stimulus representations using higher-order selection mechanisms when differences between the sensory input and the expected mental template of a genuine note are present. In this view the extended P3 could reflect serial searching of the memory representation of the note for information that confirms the counterfeit judgement. This would be consistent with eye-movement patterns found during banknote authentication that suggest confirmatory visual scanning 15 and with extant views of serial visual search strategies 35 , 36 . The later latency of the MVPA classification accuracy of the behavioural response when the banknote was counterfeit compared to genuine also suggests that genuine banknotes can be classified quickly, whereas counterfeit banknotes require protracted processing, likely involving higher-order processes such as selective attention, working memory, and long-term memory. Another possibility is that the extended P3 reflects an initial stage in a learning process whereby cues to counterfeit are selectively maintained in working memory so that long term memory representations for supporting accurate authentication in the future can be established 37 . These ideas concerning the functional significance of the extended P3 in this experiment are complementary and it is likely that this component reflects a combination of both processes.

Collectively, the ERP findings support a behavioural model of banknote authentication, called the Suspicion Initiated Model of Banknote Authentication (SIMBA 14 ). In this view, rapidly processed sensory data are matched to an internal template of an expected genuine exemplar. When discrepancies are present, signals (‘suspicions’) are used to activate greater cognitive engagement and visual search behaviour so that more information can be accumulated. When early sensory evidence of counterfeit is lacking, the expectation that the note is genuine is confirmed and the note is not further analysed. This model has important implications for consumer-directed anti-counterfeit measures used in banknote designs. Specifically, security features, i.e., hard to duplicate banknote features e.g., holograms, are used to provide an obvious indication of authenticity. Nearly all security features currently in use on banknotes require strategic scrutiny of fine details to confirm authentication. They thus require high-level cognitive engagement that the model suggests would be unlikely if other rapidly available cues on the banknote did not raise counterfeit suspicion immediately after viewing onset. The data reported here support this authentication model by revealing a cascade of processing stages. Specifically, the P1 oddball effect shows evidence that the initial feedforward sweep of information processing can differentiate genuine and counterfeit notes. However, this information alone is insufficient to support a counterfeit identification. Subsequent processing involving top-down information to guide selection and to elaborate the representation of a counterfeit is found in the P3 and especially the extended P3 response. The pattern of data reported here are thus consistent with SIMBA and have direct implications for anti-counterfeit measures related to the design of banknotes and other high value items.

Participants

26 students from the University of Birmingham (9 males; mean age = 19.0 years [SD = 0.9 years, range = 18–22 years]) took part in exchange for £16 or course credit after providing informed consent. All procedures were approved by the University of Birmingham Research Ethics Committee (ERN_17-1673) and were performed in accordance with their guidelines. Data from three participants were excluded from all analyses; two performed below chance (d′ < 0), indicating failure to follow instructions, and one withdrew early, leaving 23 complete data sets.

Apparatus and materials

A DELL XPS-15 laptop PC (screen resolution: 3840 × 2160; refresh rate: 60 Hz) using custom software created in Matlab ( The MathWorks, Inc ) and Psychophysics toolbox 38 determined stimulus presentation order, recorded data, and presented stimuli. Authentication responses were recorded using an external standard QWERTY keyboard.

All banknotes were presented as digital video images. 48 different genuine banknotes (24 x £20 and 24 x £50 notes) and 24 different counterfeit banknotes (12 x £20 and 12 x £50 notes) were used as stimuli. Genuine notes were sourced from local commercial banks and showed typical signs of prior use. Counterfeit notes, obtained from the Bank of England, had been recovered from general circulation and showed a comparable level of wear. The primary obvious security features were a foil hologram stripe (£20) and green micro-optic security stripe (£50) as shown in see Fig.  6 A. Counterfeit exemplars varied modestly from each other. Differences between counterfeit and genuine included general image resolution, contrast, and hue, as well as specific differences in the appearance of security features.

( A ) Example Images of the genuine (left) and counterfeit (right) GBP £20 (top) and £50 (bottom) notes. ( B ) Trial sequence in the Authentication Task. The trial image of the £20 note is a still from a video clip showing a genuine note. The word “specimen” was never present during the task.

To prepare stimuli for presentation, each banknote was videoed in a well-lit room with natural light sources in front of the banknotes using a Sony FDR AX33BDI camcorder, recorded in 3840 × 2160 resolution (ultra high definition). During filming, each banknote was centred in a note frame attached to a robotic arm that rotated slowly around a vertical axis. This apparatus was positioned in front of a monochrome textured board. Each video was then edited into a 300 ms clip for use in the study. For £20’s, video clips were edited to include the security feature hologram’s image to visibly change between “£” and the number “20” and for a bright reflection from the shiny strip to be visible. For £50’s, editing included visible up-down motion in the holographic stripe and no obstruction by light glare. Half of the videos showed a note rotating left-to-right; remaining videos showed a note rotating in the opposite direction. Clips were then re-sized to match the physical size of each banknote. During testing, each clip was presented in the centre of the black laptop LCD screen framed by a grey (RGB: 127 127 127) rectangle. The participant viewed the screen from approximately 50 cm.

Participants were handed a genuine (physical) £20 then £50 banknote (order counterbalanced across participants) and asked to familiarise themselves with each note for one minute in preparation for the upcoming authentication task. Next, they viewed a genuine £20 and £50 banknote video clip on the screen for 3 s, appearing as it would be presented more briefly in the upcoming authentication task. These presentations were repeated at least five times and up to 10 times if requested, allowing participants to became familiar with the appearance of genuine notes. After familiarisation, the authentication task was conducted. On each trial, the participant viewed a bright, grey rectangle for a jittered interval (average 2 s), followed by a 300 ms banknote video clip. See Fig.  6 B. At offset, the participant reported whether the banknote was real or fake by pressing the ‘z’ or ‘m’ key with their left and right index finger, respectively. The next trial began immediately after the response.

The authentication task comprised 20 blocks of 36 trials each (totalling 720 trials). Half of blocks presented only £20 s, remaining blocks presented only £50 s. Blocks alternated denomination with order being counterbalanced across participants. 20% of trials within each block presented counterfeit; order of banknote type within each block was individually pseudo-randomised. Before starting the experimental trials, six practice trials were provided using the same procedure as in the main experimental blocks except that feedback (beep for correct responses) was provided for practice trials only. The number of correct and incorrect trials for each banknote authenticity (genuine, counterfeit) used in the behavioural and EEG analyses are provided in Table 1 . The number of trials, participants, and effects size indicate that the study was sufficiently powered (> 0.80) 39 .

EEG recording

Electroencephalographic (EEG) data was collected during the authentication test. The EEG was recoded using active Ag–AgCl electrodes (BioSemi) from 32 scalp sites (FP1, FP2, AF3, AF4, F7, F8, F3, F4, FC5, FC6, FC1, FC2, T7, T8, CP5, CP6, CP1, CP2, C3, C4, P7, P8, P3, P4, PO3, PO4, O1, O2, Fz, Cz, Pz, Oz) and the left and right mastoids according to the 10–20 system (American Electroencephalographic Society, 1994). For the detection of eye movements and blinks, vertical and horizontal electro-oculogram (EOG) was recorded from electrodes placed above and below the right eye and at the outer canthi of each eye. The EEG and EOG were low-pass filtered with a fifth-order sinc filter (half-power cutoff at 128 Hz) and digitized at 512 Hz.

ERP processing

All ERP data analysis was conducted using EEGLAB 40 and ERPLAB Matlab toolboxes 41 , as follows. The EEG signals were offline referenced to the average of the left and right mastoids. The EEG was bandpass filtered offline using a Butterworth infinite impulse response filter with half-power cutoffs at 0.05 and 30 Hz and a roll-off of 12 dB/octave. The data was then down-sampled to 256 Hz.

Noisy channels were substituted by interpolating neighbouring electrode sites. Then, independent component analysis was used to estimate and remove eye-blinks and eye-movements from the stimulus presentation period of the trial using EyeCatch 42 . Finally, trials were removed if the EEG exceeded ± 100 µV in any channel between 200 ms before and 1200 ms post banknote onset: these included noisy segments (eye-blinks, movement, muscle tensing, etc.…). Trials were also excluded if the vertical EOG exceeded ± 80 µV between 200 ms prior to and 200 ms post banknote onset to ensured that the eyes were not closed when the stimuli were presented.

Averaged event-related potential (ERP) waveforms were computed by averaging trials (200 ms before the onset of the banknote to 1200 ms post onset), after they were baseline corrected to the pre-stimulus interval. The amplitude of the P3 and extended P3 components were measured on the Pz electrode site, as the mean voltage during predefined time windows. The time windows for the P3 and extended P3 components were 450–600 ms and 600–900 ms post banknote onset, respectively. The P1 was defined as the mean voltage from 50 to 100 ms post banknote onset on the Oz channel.

Multivariate pattern analysis

Pre-processing of the EEG data for the MVPA analysis was the same as for the ERP analysis. MVPA was performed on the epoched data (200 ms prior to banknote onset to 1200 ms post onset) using the ADAM toolbox 28 in Matlab. A linear discriminant classifier was trained and tested on each time point using a fivefold cross validation. The area under the curve (AUC) was used to measure classification accuracy. The classifier was first trained on the authenticity of the banknote (genuine versus counterfeit). The classifier was then trained separately for genuine and counterfeit banknotes depending on the response of the participant (genuine versus counterfeit response). Because of unbalanced trial numbers for genuine and counterfeit banknotes, the trial numbers were balanced based on the condition with the lowest trial numbers. Next, generalisation matrices were calculated using cross-classification across time. Statistical analyses for the MVPA were performed using the ADAM toolbox. Cluster-based permutation corrected 2-sided t -tests against chance (0.5) were used to analyse the classification accuracy.

Data analysis

Three 2 × 2 repeated-measures analyses of variance (ANOVAs) were conducted on ERP magnitude measures using denomination (£20, £50) and authenticity (Genuine, Counterfeit) as within-subject factors. The first ANOVA was conducted on the P1 (50–100 ms) component data at electrode Oz, the second on the P3 (450–600 ms) component data and the third on the extended P3 (600–900 ms) component data, both at electrode Pz.

To analyse the difference between conscious and unconscious neural activity to the banknotes, three further 2 × 2 repeated-measures ANOVAs were conducted on the same ERP components using authenticity (Genuine, Counterfeit) and response (Real, Fake) as within-subject factors. The data was collapsed across denomination to increase the number of correct and incorrect trials in the analysis. From these data, only participants who had more than 20 incorrect responses to both genuine and counterfeit notes were included (19 participants).

Counterfeit sensitivity was calculated as d′ for each authentication denomination. d′ was calculated as Z (hit rate)— Z (false alarm rate). Hit rate was calculated as the proportion of counterfeit notes recognised as counterfeit; False alarm rate was calculated as the proportion of genuine notes incorrectly judged as counterfeit. d′ was analysed using a paired-samples t -test comparing denominations (£20, £50). Reaction times (RT) faster than 200 ms, slower than 5000 ms, and incorrect trials were removed, then RT slower than 3 S.D above the participant x condition mean RT were removed. Remaining RT were analysed using a 2 × 2 repeated-measures ANOVA with denomination (£20 versus £50) and authenticity (genuine, counterfeit) as within-subject factors was conducted.

All follow-up pairwise comparisons were corrected for multiple comparisons using the False Discovery Rate procedure 43 . Alpha levels were set at 0.05 throughout.

Ethical approval

All methods were carried out in accordance with relevant guidelines and regulations of the Research Ethics Committee at the University of Birmingham.

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Acknowledgements

This study was supported by the Bank of Canada, the Bank of England, The Reserve Bank of Australia and US Federal Reserve Bank.

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J.R. developed the initial study concept and design. D.D. developed the MATLAB code for the experiment, conducted the testing and data analysis. J.R. & D.D. contributed equally to manuscript preparation.

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Dodgson, D.B., Raymond, J.E. Banknote authenticity is signalled by rapid neural responses. Sci Rep 12 , 2076 (2022). https://doi.org/10.1038/s41598-022-05972-8

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DOI : https://doi.org/10.1038/s41598-022-05972-8

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Rare and Valuable Banknotes from Around the World

Rare and Valuable Banknotes from Around the World – For most banknote collectors, coming across one of the rare and valuable items on this list would be a once-in-a-lifetime experience. The world’s most valuable banknotes include misprints and other production mistakes, notes printed hundreds of years ago and impossibly rare versions of existing notes.

In this two-part blog series, we’ll look at some of the rarest and most valuable notes from around the world. Worried you may never get your hands on one of these priceless artifacts of numismatic history? Noticing the shared characteristics of valuable and rare notes might help you predict the next addition to this list while it’s still affordable – a great investment!

The United States $10,000 Note

10,000 Dollars United States’s Banknote

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The 1891 United States Red Seal $1000 Note

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Most USA residents have never seen a bill larger than $100 – but that doesn’t mean that they don’t exist. When the nation stopped printing $10,000 bills in 1945, it also discontinued $1000 and $5000 bills that were in circulation previously. This note, printed in 1891, sold at an auction in 2013 for just over $2.5 million, making it one of the most expensive bank notes ever sold. The note, which depicts Major General George Gordon Meade, is over 100 years old and is thought to be one of only two still in existence.

The 2007 Canada $1m Coin

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The Zanzibar 1908 20 Rupees Banknote

This banknote was sold as part of a small collection at a public auction in 2011, fetching a sale price of $225,000 USD. It certainly can be viewed as a rare and valuable Banknotes. Although it was printed just over 100 years ago, the specimen is officially the world’s most expensive African banknote. Twenty rupees was a lot of money when the notes were printed, and today, collector’s value these specimens for their age, rarity, and intricacy of design.

Brunei’s $10,000 Banknote – The Most Expensive Still in Circulation

10,000 (10000) Ringgit Brunei’s Banknote

Brunei may not be selling any of its notes at auction for millions of dollars, but the nation does boast the most valuable bank note currently in print. Freshly redesigned in polymer in 2006, the $10,000 note shows no signs of being discontinued. Unlike other nations that print high-denomination currencies when their money is weak, Brunei’s $10,000 note is worth a whopping $7,122 at market. A couple of them could buy you a car, or you could save 3 or 4 and put a down payment on a house.

We hope you’re loving our look at bank notes that are known especially for their rarity and value – stay tuned for part two of this series where we’ll keep exploring some of the rarest, oldest, and most valuable bank notes from around the world.

11 thoughts on “ Rare and Valuable Banknotes from Around the World ”

I have an Australian $50.00 poylemere note that has a genuine stamp of GB1 on the Edith Cowan side. There is no information on this online except for the information that GB1 is to do with the poylemere coating. Await a reply. Michelle

I have a 10,000 Dinars from Central Bank of Iraq,,a 250 Dinars from Central Bank of Iraq, a 50 Dinars from Central Bank of Iraq and a 5 Dinars from Central Bank of Iraq.. are they worth anything?

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What is the value of 1000000 Turkish series note .

Hello we do not offer appraisal services.

Your banknotes are worth something that’s for sure.

Thanks we hope you enjoyed our article 🙂

I have a 50 Escudos banknote from Timor, would it be considerd rare. Dated 1967

Sorry unfortunately we do not do evaluations on customers banknotes. However you can search on ebay and that can tell you market rate.

Hi, I have OLD Bank Note Proofs (one sided) from multiple countries, are these valuable at all?

Sorry but, we don’t do valuations on banknotes. If you can find it online thats what the market rate is.

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• While banknotes used to be backed by precious metals such as gold and silver, in 1971, the United States government went off the gold standard, making American banknotes a fiat currency that is backed instead by good faith.

How Banknotes Work

Before modern societies and financial systems were set up, people used valuable objects, such as gold and silver, to pay for goods and services through bartering. Eventually, paper money and coins replaced these physical assets as representative currency. When this happened, precious metals backed the new currencies to give it credibility.

At present, only the government backs banknotes . Although in earlier times commercial banks could issue banknotes, the Federal Reserve is now the only bank in the United States that can create banknotes and mint money. Worldwide, billions of financial transactions use banknotes every day.

Historically, U.S. citizens could exchange U.S. government-issued paper money for gold or silver. This bimetallic standard system consisted of paper currency in a fixed ratio with gold and/or silver. However, in 1964, the U.S. government gradually began to halt the bimetallic standard; in 1971, the U.S. went off the gold standard altogether. The decision created a pure fiat currency , which the government supported only with its good faith in its ability to pay off any debts.

Fiat money derives its value from the relationship between supply and demand, not the value of the currency’s physical material. Since fiat money is not linked to physical reserves, it risks becoming worthless, due to hyperinflation . For example, if in a distant future U.S. citizens lose faith in the U.S. dollar bill, this paper currency will no longer hold value. Luckily, the likelihood of the U.S. dollar collapsing is very low.

Many use the terms banknotes, currency notes, and bills interchangeably. While both are promissory notes, many use currency notes more frequently for common dealings.

While it is common in some cultures to keep one's savings in the form of banknotes, this poses inflationary risk because cash loses buying power over time. If you have a large amount of cash on hand, a savings account or certificate of deposit can help you earn a small amount of interest.

Polymer Banknotes and the Bank of England

In 2013 the Bank of England considered introducing polymer banknotes. These plastic-like banknotes, which Canada and many other nations also use, are easier to clean and harder to counterfeit than paper notes.

The pros of introducing polymer banknotes also include their enhanced security features, reduced replacement costs (as polymer lasts two and a half times longer than paper), waterproofing, dirt-resistance, and overall lower negative environmental impacts.

Cons to introducing polymer banknotes into Britain’s monetary system included a higher upfront manufacturing cost, counting difficulties – given that the material is slipperier than paper — challenges in folding the new material, and questionable compatibility with existing vending machines and auto-payment systems.

What Is the Difference Between a Banknote and Regular Money?

Today, there is little difference between the term "banknote" and other types of currency. Historically, the term "bank note" originated from the historical period when banks could issue their own paper currency, backed by the value of their gold and silver deposits. Today, the right to print notes is usually reserved to a country's central bank, although there are some countries that delegate that authority to commercial banks.

How Do You Tell if a Banknote Is Real?

Most central banks implement a combination of anti-counterfeiting measures in printed notes, such as raised type, watermarks, and threaded paper. These will vary depending on the currency and the date of issue. For U.S. currency, the Secret Service provides a helpful list of existing security measures.

How Can I Replace a Damaged Banknote?

If you have mutilated U.S. currency, you can freely redeem your notes by sending them to the Bureau of Engraving and Printing.

Banknotes are paper bills that are used as currency. The first banknotes were promissory notes, backed by the gold and silver in the bank's vaults. Today, banknotes are the main manifestation of physical currency, although they may eventually be replaced by digital payment systems.

Congressional Research Service. " Brief History of the Gold Standard in the United States ." Page 12 of PDF.

U.S. Secret Service. " Know Your Money ."

Bureau of Engraving and Printing. " Mutilated Currency Redemption ."

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• Published: 13 November 2021

Enhancing banknote authentication by guiding attention to security features and manipulating prevalence expectancy

• Frank van der Horst   ORCID: orcid.org/0000-0003-3076-8478 1 ,
• Joshua Snell 2 , 3 &
• Jan Theeuwes 2 , 3  

Cognitive Research: Principles and Implications volume  6 , Article number:  73 ( 2021 ) Cite this article

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All banknotes have security features which are intended to help determine whether they are false or genuine. Typically, however, the general public has limited knowledge of where on a banknote these security features can be found. Here, we tested whether counterfeit detection can be improved with the help of salient elements, designed to guide bottom-up visuospatial attention. We also tested the influence of the participant’s a priori level of trust in the authenticity of the banknote. In an online study ( N  = 422), a demographically diverse panel of Dutch participants distinguished genuine banknotes from banknotes with one (left- or right-sided) counterfeited security feature. Either normal banknotes (without novel design elements) or banknotes that contained a salient element (a pink rectangular frame) were presented for 1 s. To manipulate the participant’s level of trust, trials were administered in three blocks, whereby at the start of each block, participants were instructed that either one third, one half, or two thirds of the upcoming banknotes were counterfeit (though the true ratio was always 1:1). We hypothesized (i) that in the presence of a salient element, counterfeits would be better detected when the location of the salient element aligned with the location of the counterfeited security feature—i.e. that it would act as an attentional cue; and (ii) that this effect would be stronger with lower trust. Our hypotheses were partly confirmed: counterfeit detection improved with ‘valid cues’ and decreasing trust, but the level of trust did not modulate the cueing effect. As the overall detection performance was rather poor, we replicated the study with a sample of university students ( N  = 66), this time presenting stimuli until response. While indeed observing better overall performance, all other patterns were replicated. Our results provide evidence that attention can be guided to enhance banknote authentication.

Introduction

Typically, people accept banknotes as change from another person or at a point-of-sale without consciously verifying authenticity (Klöne et al., 2019 ). Reasons for not checking authenticity are that counterfeit rates are extremely low, and that people trust the retailer (van der Horst et al., 2017a ). Indeed, authentication may take place in a limited number of cases; for example, when the cash handler has encountered counterfeit banknotes before, or when the paper of a particular note feels somewhat unusual. Also, when one does not trust a particular transaction (e.g. an online purchase involving cash) one may check the authenticity of the banknote. A more practical constraint is that the general public has little knowledge of how to authenticate banknotes. On average, a person can mention two security features, but does not know what these features look like exactly, and where on a banknote these features may be found (van der Horst et al., 2017a ). For instance, 69% of the general public knows that a euro banknote contains a watermark, but only 6% knows what image the watermark depicts (De Nederlandsche Bank, 2021a , 2021b ). The next most known security feature is the hologram foil, mentioned by 39% of the public. The emerald number can be recalled only by 2% of participants.

As a consequence, a good deal of counterfeited banknotes goes undetected. To illustrate, van der Horst et al. ( 2017b ) reported that around one in every five counterfeits is missed, in spite of the fact that participants were actively authenticating and were granted all the time they needed for this authentication task. It would not seem unreasonable to assume that the proportion of undetected counterfeits must be decidedly higher in everyday life, where cash handlers are not explicitly instructed to authenticate.

Yearly, the Eurosystem removes around 560 thousand counterfeits from circulation (out of a total of 24 billion banknotes; ECB annual report 2019). For an overview of the most prominent public security features, as indicated by the Nederlandsche Bank (DNB), see Fig.  1 .

Source: DNB website ( www.dnb.nl/echtofvals )

Instructional image on how to check the most prominent security features of a EUR 50 banknote quickly.

Yet another reason for not checking the authenticity of a banknote may be that the authentication process itself would constitute a socially awkward or uncomfortable situation—all the more fuelled by the fact that aforementioned lack of knowledge would likely make the authentication process a long one. If cash handlers were able to authenticate banknotes more quickly and covertly, it may well be that fewer counterfeits would go unnoticed. Additionally, if banknotes were authenticated more easily, perpetrators may be less inclined to use counterfeit banknotes in the first place.

In short, members of the public are rarely inclined to check a banknote for its authenticity, but when they do, they lack the capability to do it properly. Here we investigated whether counterfeit detection can be improved with the addition of novel, salient visual elements, designed to guide visuospatial attention to critical locations. Additionally, we assessed the impact of one’s a priori trust on attentional orienting.

Our hypotheses were guided by two distinct fields of study. The attention literature led us to reason that a counterfeited security feature should be detected more readily when attention is directed to the security feature’s location. One way to ensure that attention is directed to a critical location is to introduce a visually salient element near the location of the security feature such that attention is captured towards the critical location in a bottom-up way (e.g. Theeuwes, 2010 ; Wolfe et al., 2003 ). The hypothesized beneficial effect on counterfeit detection performance of having a salient element near a security feature, would be analogous to an attentional cueing effect (Posner, 1980 ). With respect to one’s a priori of trust, we reasoned that lower levels of trust would increase overall performance (due to increased effort). We were largely agnostic with respect to interactions between trust and cue validity. On the one hand, one might argue that increased effort (induced by low trust) would cause stronger attentional orienting and consequently stronger capture by salient design elements. On the other hand, an increased contribution of top-down attention might reduce the strength of bottom-up attentional capture. Let us now turn to these attentional dynamics.

Attentional processes in counterfeit detection

Cash transactions at a point-of-sale are generally performed quickly and automatically (van der Horst & Matthijsen, 2013 ). People do not give themselves time, or might feel embarrassed when scrutinizing the banknote (De Heij, 2017 ).

To authenticate a banknote properly, a good strategy is to direct attention to the security features. Attentional orienting can proceed in a bottom-up and top-down manner. Bottom-up attention is usually deployed reflexively due to the characteristics of the scene and stimulus saliency (e.g. Theeuwes et al., 2003 ), although the capture of attention can be prevented via an inhibitory mechanism that suppresses the salient stimulus (Luck et al., 2021 ). Top-down attention, which is thought to underly that inhibition, is usually deployed voluntarily in line with one’s tasks and goals (Egeth & Yantis, 1997 ). However, top-down authentication of banknotes is likely hampered by the handler’s aforementioned lack of knowledge.

It would therefore be ideal if security features were to capture attention in a rapid bottom-up manner (e.g. Theeuwes, 2019 ). It is worth noting that there has recently been a marked rise of simplified counterfeits without (mimicked) security features (Deutsche Bundesbank, 7–8-2020), suggesting that if attention were directed immediately and briefly to the relevant location on a banknote this could improve counterfeit detection. This underlines the importance of guiding banknote users’ attention to security features.

It may come as no surprise that saliency is a well-known concept among developers of banknote security features. For instance, nano-optic display technology features deliver a sense of movement, 3D depth, and multiple colours. According to manufacturers these technologies enable a wide array of custom design options to both capture and hold the user’s attention as they inspect and authenticate a banknote (16-11-2020, https://www.nanosecurity.ca/banknote-security/ ). However, to date there is no scientific dissemination about the effectiveness of security feature saliency. Furthermore, one must take into account the possibility that with increased saliency of one security feature, attention may increasingly be directed away from other security features. One challenge is thus to achieve optimally balanced saliency across features—a challenge enlarged by the fact that features differ from each other in terms of shape and size.

A potential solution—and the focus of this study—is to display a single type of salient element near each security feature. As such, the security features themselves can stay as they are, while the novel salient design element may become an established marker for areas worthy of inspection.

Although there is a lot of research suggesting that attention can be guided with the help of salient visual elements (e.g. Theeuwes, 2010 ), we must nonetheless be aware of one potential constraint. It is known that the most salient elements in a display typically receive attention first—irrespective of whether they are relevant or irrelevant (Wang & Theeuwes, 2020 ). Hence, if the salient element is at the same location as the security feature—as in the case of, say, a pink frame around the banknote’s emerald number—attention would be at the right location; but would it predominantly be directed to the pink frame, or to the emerald number itself? In the former scenario, the salient element would be helpful in roughly guiding attention (e.g. attention would be oriented to the right quadrant of the banknote), whilst interfering at a more detailed level (e.g. attention would be focused on the pink frame rather than on what is in the frame).

We chose the colour pink (desaturated red) for the frame, because of its saliency. In an experiment conducted by Drelie Gelasca et al. ( 2005 ) participants had to rank 12 colours in terms of saliency. The colours that had much more hits were red, yellow, green and pink. Those of lower saliency seemed to be light blue, maroon, violet and dark green. Also, in a colour experiment in which two groups searched for desaturated targets among saturated and white distractors, the conclusion was that the pink and peach targets have an advantage over the green, blue, and purple targets concerning reaction times (Kuzmova et al., 2008 ).

The impact of trust

As noted earlier, we expect that persons who have high trust in the authenticity of banknotes, for example because they assume that the counterfeit rate is low, perform worse than persons who expect a higher counterfeit rate. This hypothesis is based on the ‘prevalence-effect’. Observers tend to miss a disproportionate number of targets when these targets are rare (Wolfe & Van Wert, 2010 ). In everyday life, the prevalence of counterfeits is very low. The general public mentions this as an important reason for not authenticating (Klöne et al., 2019 ).

Lau and Huang ( 2010 ) found that the prevalence effect depends on past experience, not on future prospects. In their study, participants were told either that targets would be frequent (50%) or rare (10%), and both these instruction types were provided in settings where the true prevalence was either 50% or 10%; (hence, prevalence and the expectancy thereof were orthogonally manipulated). As it turned out, the error rate depended not on the instructions given but on the true target prevalence of the blocks. However, it might have been the case that participants simply did not believe the instructions (i.e. that expectancy was not successfully manipulated).

In fact, other research suggests that both target repetition and target expectancy play a role in the prevalence effect (Godwin et al., 2016 ). In the study of Godwin et al., one group of participants searched for low and high-prevalence targets of one particular colour throughout the experiment, while another group searched for one target colour on high-prevalence slides and a different target on low-prevalence slides. As such participants received differential levels of target repetition across the lower and higher-prevalence targets. An effect of prevalence emerged in both groups, although it was weaker in the single colour condition than it was in the alternating-colour condition, suggesting that both target repetition and target expectancy play a role in the prevalence effect.

Previous studies have shown that prevalence expectancy can simply be influenced by task instructions. For example, in their investigation of lesion detection on chest radiographs, Nocum et al. ( 2013 ) found that expectations of a higher abnormality-prevalence rate, as induced by instructions, impacted doctors’ perceptual sensitivity and visual search patterns, even though observers received the same stimulus material.

In the current study, we manipulated the expectancy of prevalence, which was assumed to affect top-down attention, and manipulated the presence or absence of a salient element around security features, which was assumed to affect bottom-up attention. The manipulation of expectancy is particularly important as it is one of the underlying factors of the trust one has in the payment system. The rationale is that people who have low trust in the authenticity of banknotes expect that the counterfeit rate is relatively high are more likely to invest more effort in authentication and thereby, to enhance authentication (van der Horst, et al., 2020a ).

The present study

To summarize the above, typically the general public does not authenticate banknotes because they trust the banknote to be genuine and because they have insufficient explicit knowledge about which locations on the banknote inform its authenticity. Therefore, in this study, we examined whether salient elements around security features may help the public in authenticating a banknote at a quick glance. It is important to determine whether authenticating can be done rapidly because cash transactions typically occur within a very brief time frame (van der Horst et al., 2020b ). We hypothesized that displaying a pink frame around a counterfeited security feature would lead to better counterfeit detection. This manipulation is to some extent analogous with the classic Posner exogenous cueing paradigm (Posner, 1980 ), in which targets are typically detected faster and more accurately when a cue is valid than when it is invalid.

Importantly, we did not instruct our participants on the existence and location of security features, as the general public is not trained either. Below it will be seen that overall detection scores were indeed not very high. However, our focus is not the performance per se, but the difference between having a salient element near to versus away from, the counterfeited feature, thought to operate as a valid versus invalid attentional cue, respectively. By directing the participants attention to a counterfeited feature, we expect to improve their ability to categorize the banknote as counterfeit.

Participants

In order to have a representative sample of the general public in the Netherlands, we made use of the LISS panel (longitudinal Internet Studies for the Social Sciences) run by CentERdata at Tilburg University. This panel is representative of the general population in the Netherlands and comprises around 5000 households in the Netherlands. We aimed for a net sample of 400 participants, but in total 451 participants participated in the experiment. The panellists were 16 years and older. They received a small monetary compensation (EUR 7.50, real money) for their expenses (internet use and time).

The experiment followed a 3 × 3 × 4 within-subjects design, with the following factors: Cue (left, right, none); Trust  (high, mid, and low, corresponding to low, mid, high counterfeit expectancy); Authenticity (counterfeit element left, counterfeit element right, genuine, genuine); genuine is mentioned twice to have the same number of genuine versus counterfeit trials.

The test set consisted of images of genuine euro banknotes that were taken out of circulation and visually altered (counterfeit) versions of the same banknotes. We created counterfeits by replacing a single genuine security feature by a cut-out of a counterfeited security feature. There were two types of counterfeited security features: the hologram (silvery stripe) that is positioned at the right side of the banknote and the emerald number that is positioned at the left side of the banknote, corresponding to the counterfeit element right and left conditions. The cut-outs were obtained from counterfeits taken out of circulation by De Nederlandsche Bank. We used cut-outs of simple ink-jet counterfeits instead of the ones printed with offset techniques, as these are the most prevalent. According to DNB’s national counterfeit analysis centre, the counterfeited elements in our test set were of average mimicking quality, which means that a counterfeited element can be noticed visually by the average person when attention is directed to it.

Additionally, for all banknote stimuli we created versions with a salient pink rectangle framing either the left or right-sided security feature. Because the hypothesized effects of having a salient element near to or away from a counterfeited feature are interpreted as attentional cueing effects, versions of counterfeited notes with salient element at the same versus different location as the counterfeited feature represent the Valid Cue and Invalid Cue conditions, respectively. We chose the colour pink because it is rated as a particularly salient colour (e.g. Drelie Gelasca et al., 2005 ; Kuzmova et al., 2008 ).

We used both EUR 20 and EUR 50 banknotes (denomination not being considered an experimental factor). The complete stimulus set consisted of 24 images, i.e. 2 Authenticity (genuine/counterfeit) × 3 Cue (left/right/no cue) × 2 Security feature (hologram/emerald number) × 2 Denomination (EUR 20/50). Denominations EUR 20 and 50 were used because these are by far the most used and counterfeited ones (press release DNB, 22 January 2021). The denominations EUR 20 and EUR 50 were manipulated according to the same method described above. Figure  2 shows examples of manipulated banknotes.

Examples of manipulated banknotes that are part of the test set. The banknotes on top contain a counterfeited emerald number: top-left with a pink cue around the counterfeited emerald number; top-right with the pink cue around a genuine hologram. At the bottom, banknotes with a counterfeited hologram: left-bottom a pink cue around the counterfeited hologram; right-bottom with a cue around a genuine emerald number. The two banknotes on the left are validly cued (the cue is located near the feature that is counterfeited). The two banknotes on the right are invalidly cued: the cue is near a genuine feature, while the counterfeited feature is at the other side

Clearly, the proportion of genuine and counterfeit banknotes in the test set (1:1) is quite different from the probability of encountering a counterfeit in real life, which is roughly 0.003% (ECB, 2019 ). In addition, one’s perceived likelihood that one will receive a counterfeit does not directly reflect real-world prevalence either. Instead, we would argue that counterfeit expectancy is a function of immediate context, and that the subjective biases that stem from this context are much more variable than real-world counterfeit prevalence. It is these variations in subjective prevalence expectancy that are studied here.

Participants were invited to perform the test online on their own computers. For this reason, there was little control over the degrees of visual angle of our stimuli.

In the instructions participants were told that DNB wanted to test some design elements and that therefore a pink rectangle could be seen on the majority of banknotes. However, according to the instructions these new design elements would have no relation to whether the note was genuine or not. Next, participants were informed that banknotes would be presented for one second. They were instructed to authenticate the banknotes by typing a ‘z’ for genuine and ‘/’ for counterfeit after the banknote was presented. They were instructed to respond as accurately as possible. They had a maximum of 4000 ms to respond (after which the response would be considered an ‘error’). Banknotes were presented centrally, albeit with minor jitter (ranging up to 40 pixels) in the banknote’s x and y coordinates, so as to prevent participants from developing oculomotor strategies. An overview of the trial procedure is shown in Fig.  3 . To get acquainted with the procedure, participants performed 12 practice trials that were not included in the data analyses.

Example of a trial. Each trial started with a fixation dot in the centre, for 500 ms, followed by a banknote (either EUR 20 or EUR 50, either genuine or counterfeit, either with a cue or not). The display duration was 1000 ms. The information regarding the ratio of counterfeits was varied between blocks. If participants failed to press a key within 4,000 ms from stimulus onset, the trial was logged as a time-out

The participants’ trust in banknote genuineness was manipulated between blocks. All 24 images were presented three times, in three blocks (presented in random order for each participant). Every time before the start of a block, participants were informed on the expected ratio between genuine and counterfeits for the upcoming block: (i) two out of three, (ii) even, and (iii) one out of three. In reality, the genuine vs. counterfeit ratio was always 1:1.

At the end of the experiment, participants received feedback regarding their performance: a percentage correct was provided for all three blocks. Participants were invited to fill in a short survey for demographics, colour blindness and cash experience in working life (for the purpose of post hoc analyses). The experiment took approximately 10 min.

All trials with a time-out were removed. In case this resulted in removing more than a third of a participant’s trials, the data of this participant were removed altogether, as this indicates that the participant was not able to perform the task properly. In total, 29 participants were removed, constituting 9.1% of the data. The results of the remaining 422 participants were used.

To reiterate, the experiment included the following factors: Cue Validity ( valid vs. invalid cues ) and Trust ( low, mid- and high levels of trust). These variables allowed us to rely, in part, on measures derived from Signal Detection Theory (SDT). The ability to discriminate genuine banknotes from manipulated banknotes is called sensitivity (d’), which can be estimated by deducting the z-transformed probability of false alarms (i.e. incorrectly classifying a genuine banknote as being counterfeit) from the z-transformed probability of hits. A d’ score of 0 corresponds to a complete inability to distinguish genuine banknotes from counterfeits. According to Raymond ( 2017 ), a d’ of 1.25 represents decent sensitivity in banknote authentication. The maximum d’ score that can be obtained in this study is 3.92.

Importantly, while d’ can be calculated when inspecting main effects of Trust (i.e. irrespective of cueing condition), this is not the case when inspecting main effects of Cue Validity (i.e. irrespective of level of trust). This is because the cue valid and invalid conditions solely contain counterfeit banknote trials (indeed, consider that there is no such thing as a validly cued genuine banknote), and therefore one cannot conjure a false alarm rate required for the calculation of d’. Hence, in all analyses that involved the Cue Validity factor, we simply relied on accuracy (the SDT-equivalent of which would be the hit rate, retrieved from counterfeit banknote trials). Our central analysis (reported in " Central analyses " section) was thus a 2 × 3 repeated measures analysis of variance (ANOVA) with Cue Validity and Trust as factors, and accuracy as dependent variable.

We nonetheless also analysed Trust in isolation (" Verifying the manipulation of trust " section), as we could retrieve not only d’, but also the response bias (i.e. the extent to which one response is more likely to be given than another), or β, when inspecting this variable separately. The β measure, calculated by dividing the z-transformed probability of hits by the z-transformed probability of false alarms, provides an important verification of the effectiveness of our Trust manipulation. That is, if participants took the block instructions to heart, we expected them to have marked a larger portion of genuine banknotes as counterfeit upon being warned for a high counterfeit prevalence (although actual prevalence did not vary across conditions). At the same time, we may expect them to mark a low number of counterfeits as being genuine. Upon being warned for a low counterfeit prevalence, we would expect these patterns to be inversed. In short, if our Trust manipulation was indeed effective, we expect that β would be higher (i.e. more conservative) in the high-trust than in the low-trust condition.

Verifying the manipulation of trust

Repeated measures ANOVAs were used to analyse main effects of Trust on d’ and β. Overall, sensitivity did not increase linearly with a decrease in Trust ( F (2,421) = 2.131, p  = 0.119). We did, on the other hand, observe a numerical effect of Trust on β that approached significance: ( F (2,421) = 2.437, p  = 0.088), with a more conservative response strategy in the high-trust than in the low-trust condition: i.e. lower levels of trust aided counterfeit detection, but, at the same time, caused a higher proportion of false alarms. From these results we conclude that the way in which we manipulated trust was effective.

Central analyses

A repeated measures ANOVA was run with Cue Validity and Trust as factors and Accuracy as dependent variable. In line with our hypotheses, valid cues led to better accuracy than invalid cues: F (2,421) = 4.969, p  = 0.007, η 2 p  = 0.012. Again, we also observed a main effect of Trust ( F (2,421) = 3.916, p  = 0.020, η 2 p  = 0.01), with better counterfeit detection at lower levels of trust; (however, given the absence of effects in d’ and the reversed effect for genuine banknotes, as reported in " Verifying the manipulation of trust " section, it can be argued that this particular effect reflects a shift in β, rather than a change in overall performance). Trust did not modulate the effect of Cue Validity: F (4,421) = 0.621, p  = 0.648. Figure  4 shows the average scores for the nine conditions.

Average accuracy per level of trust (low, mid, high) and cueing condition. Both a low trust in the authenticity (i.e. a high expectancy on the number of counterfeits) and valid cueing led to better performance. Error bars depict 95% confidence intervals

Evidently, overall authentication performance was quite poor in this population sample. In order to determine whether the task was too difficult, we calculated the average sensitivity scores in the no-cue condition, since this condition provides a baseline (without novel design elements) and as such can be compared to the study of van der Horst et al. ( 2020a , 2020b ). We observed a sensitivity of d ’ = 0.386, which is indeed decidedly lower than the sensitivity d ’ = 1.05 observed in the study of van der Horst et al. ( 2020a , 2020b ). Although overall sensitivity was quite low, it was significantly above chance-level ( t (421) = 11.274, p  ≤ 0.001). It is also worth noting that the low sensitivity was unlikely to be driven by a lack of expertise: people who responded to have experience with cash in a professional setting did not perform differently from the others ( t (420) = 1.269, p  = 0.205).

We reckon that recognizing a single fake element in an image of a banknote that is exposed for only one second might be difficult for non-trained members of the general public. Crucial in this regard is the fact that the salient design element, when acting as a valid cue, significantly improved performance.

We wanted to examine if the observed effect of cueing would also hold if the task was less difficult. For this reason, we decided to run the same experiment with a group of 66 psychology students and this time presenting the images of the banknotes until response. The results of this replication experiment are presented in the Appendix. Importantly, while the overall performance in this population sample was indeed better, we replicated all effects of interest (the bias of participants increased with a lower trust in the authenticity of banknotes: F (2,66) = 3.639, p  = 0.029). Just like the experiment with participants from the CenTErdata panel, we found main effects for accuracy per cueing validity ( F (2,66) = 4.565, p  = 0.012), η 2 p  = 0.07 and trust ( F (2,66) = 4.304, p  = 0.015), η 2 p  = 0.06 and no interaction between these factors: F (4,66) = 0.989, p  = 0.414. In addition trust affected sensitivity scores adversely: F (2,66) = 4.103, p  = 0.019.

General discussion

The goal of this study was to investigate whether salient design elements, intended to direct attention to the location of security features, would aid banknote authentication accuracy. In our experiments, pink frames around a counterfeited security feature were expected to act as a cue, akin to attentional cues in classic tasks such as Posner’s cueing paradigm ( 1980 ). Similarly, a pink frame around a genuine security feature, when at the opposite side a counterfeited security feature was present, was expected to act as an invalid attentional cue. Participants were not instructed to react to these salient elements; they were only told that DNB wanted to test some new design elements. Across two experiments we confirmed our expectations. Banknotes with a salient element around the counterfeited feature location yielded better detection than banknotes with an ‘invalid cue’ (i.e. a salient element at a different location). These results provide a proof-of-concept that salient novel design elements can aid banknote authentication.

We also found that lower levels of trust aided counterfeit detection, but, at the same time, caused a higher proportion of false alarms (" Verifying the manipulation of trust " section). It is worth considering that although high counterfeit detection rates are undoubtedly beneficial, effectuating these by means of lowering trust would imply extensive examination processes (i.e. more false alarms) and likely less smooth functioning of the cash payments system. Central banks may want to consider this particular finding when they issue press releases informing the public about counterfeit prevalence. In relation to this, Lau and Huang ( 2010 ) have argued that instructions alone might not be very effective in reducing error rates in real-life low-prevalence contexts, such as airport baggage screening or counterfeit banknote detection. Instead these authors have argued for randomly distributing ‘pseudo-targets’. This would imply an artificial increase in prevalence, and the experience gained with such pseudo-targets would reduce the chance of missing actual targets. Applying this idea to the realm of banknote authentication, central banks might consider purposefully bringing counterfeits into circulation, which, upon being spotted and reported, would yield a reward. Naturally, discussions of the legal constraints surrounding such operationalizations of trust and prevalence are beyond the scope of this paper.

The average sensitivity or d ’ in the no-cue (baseline) condition in the present experiment was 0.386. A d’ of 0 corresponds to a complete inability to distinguish genuine banknotes from manipulated banknotes; and, according to Raymond ( 2017 ), a d ’ of 1.25 represents decent authentication sensitivity. Previous research (Van der Horst et al., 2020a , 2020b ) showed a higher average sensitivity ( d ’ = 1.05) for the general public in a task similar to the present one (i.e. participants had to detect counterfeit banknotes that were presented for one second on a screen). There are however also important differences between the two experiments. Firstly, participants encountered novel design elements in the present study, which they ought to treat as being non-informative about the banknote’s authenticity. Secondly, in the present study counterfeit banknotes contained only one counterfeit element, the emerald number or the hologram. Lastly, the counterfeit quality may have differed between the studies. These factors possibly made the distinction between genuine and counterfeit banknotes smaller than in the study of van der Horst et al. ( 2020a , 2020b ).

In our replication experiment with psychology students ( N  = 66) that saw the stimulus until response, overall performance was decidedly better ( d ’ = 1.73 in the baseline condition). The pattern of positive effects on counterfeit detection by validly cueing and low trust was also found in this replication experiment.

The present findings demonstrate a possible role for bottom-up saliency to aid banknote authentication.

One potential caveat, however, is that attending to one security feature (helped by a salient element) may come at the cost of not attending to another, equally important security feature. Further tests of our hypotheses may involve comparing the authentication of banknotes without pink frame, against banknotes with multiple pink frames (i.e. one around each security feature). If our claims hold, then the pink rectangles should facilitate quicker serial processing of all relevant locations on the banknote, and thus better performance as compared to banknotes without pink rectangles.

Lastly, while saliency should help in finding the security features, what to do next—i.e. how to use these security features for successful authentication—remains a challenge. Further research on making the security features more intuitive may thus be beneficial for counterfeit detection.

In conclusion, the present findings suggest that salient design elements may aid counterfeit detection. This cueing effect is also shown for perceptual sensitivity measures such as accuracy and d’ (Bashinski & Bacharach, 1980 ; Theeuwes & Van der Burg, 2007 ). Additionally, as low levels of trust positively impacted authentication, we posit that the general public would benefit from increased awareness about the existence of counterfeit banknotes.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank our colleagues from de Vrije Universiteit and De Nederlandsche Bank for their assistance in this project.

Significance statement

Because the general public has little knowledge on how to authenticate banknotes, this study investigated whether the introduction of novel, salient design elements would be helpful in detecting counterfeits. Also we tested the influence of trust in banknote authenticity on counterfeit detection. Two lessons can be learned here. Firstly, as lower trust yields better authentication accuracy, central bankers may see merit in raising awareness about the existence of counterfeit banknotes. Secondly, our findings provide a proof of concept for the idea that bottom-up saliency can be used to aid banknote authentication.

JT and JS were supported by the European Research Council (ERC), respectively, Grants 833029 [LEARNATTEND] and H2020-MSCA-IF-2018 833223. During the present study FvdH was paid his salary by his employer De Nederlandsche Bank.

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FvdH designed the study under supervision of JT and JS. FvdH analysed the results. The data analysis was under supervision of JT and JS. All authors contributed to the writing of the document and have approved the submitted version. All authors read and approved the final manuscript.

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Correspondence to Frank van der Horst .

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Replication experiment, presentation time increased

In line with our expectations, the 66 students, who were granted a longer presentation time, performed better than the panel. The average sensitivity for the no cue condition was 1.734, definitely higher than the sensitivity score for the CenTErdata panel of Dutch participants (0.386). This score is also considerably higher than a sensitivity score of 1.25, which is the norm that Raymond ( 2017 ) proposed for representing a reasonably good performance.

The influence of trust on the authenticity of banknotes was calculated with a GLM repeated measures. In this experiment higher trust influenced sensitivity scores negatively: F (2,66) = 4.103, p  = 0.019 (Fig.  5 ). The bias of participants increased with a lower trust in the authenticity of banknotes: F (2,66) = 3.639, p  = 0.029. This means that when the participants have low trust and expect a high ratio of counterfeits the criterion is also high. Such a bias is called conservative, i.e. not willing to make that much false alarms and taking the chance of lower hits. Conversely, a low expectancy on the number of counterfeits leads to a more liberal criterion, i.e. that participants made both more hits and false alarms. See Fig.  6 . Just like the experiment with participants from the CenTErdata panel, we found main effects for accuracy per cueing validity ( F (2,66) = 4.565, p  = 0.012), η 2 p  = 0.07 and trust ( F (2,66) = 4.304, p  = 0.015), η 2 p  = 0.06 and no interaction between these factors: F (4,66) = 0.989, p  = 0.414 (Fig.  7 ).

Average authentication sensitivity scores per condition of trust in the authenticity of a banknote (low, mid, high). Presentation time is until response. The sensitivity scores of participants significantly changed when the expectancy of the ratio of counterfeits was varied. Error bars depict 95% confidence intervals

Average bias scores per level of trust in the authenticity of a banknote. When images were presented until response, a high trust has led to a more conservative bias, i.e. a lower tendency to declare a banknote a counterfeit. Error bars depict 95% confidence intervals

Average accuracy per level of trust (low, mid, high) and per cueing condition (invalid, no cue, valid). Presentation time is until response. Both a low trust in the authenticity (i.e. a high expectancy on the number of counterfeits) and valid cueing led to better performance. Error bars depict 95% confidence intervals

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van der Horst, F., Snell, J. & Theeuwes, J. Enhancing banknote authentication by guiding attention to security features and manipulating prevalence expectancy. Cogn. Research 6 , 73 (2021). https://doi.org/10.1186/s41235-021-00341-x

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Fraudsters are brazenly advertising fake banknotes on social media, currency firm warns

F raudsters are brazenly advertising counterfeit banknotes on social media, even offering 'bulk buy' discounts for their fake cash, to the most vulnerable,  a major currency firm warns. 

Social media sites, including Facebook, Instagram and X are awash with carefully targeted adverts offering free postage and discounts on counterfeit notes.

The adverts show wads of £10 and £20 notes as well as high-denomination Euro notes, claiming the fake cash is realistic enough to pass security checks.

Criminals are selling fake notes online at a fraction of the face value of what real notes are worth, according to exclusive research for This is Money by No1 Currency. 

One criminal was selling £14,000 of counterfeit notes for £1,200, and £150,000 of the fake cash for £10,000. 

This is Money's own research shows one criminal with wads of 'top quality polymer bank notes' advertising on Facebook in recent weeks, while another had been posting since February. 

In 2023, typically less than 1 in 40,000 banknotes were counterfeit, according to the Bank of England.

However, research by travel money firm No1 Currency found that some scammers are paying for sponsored posts to appear on the social media timelines of vulnerable people.

The cookies which record which websites you use help to deliver the adverts you see on social media, so those struggling to make ends meet are especially likely to be targeted.

The firm says that those behind the ads are committing an offence, while the unwary who part with their money to buy the fake notes risk either being scammed if their notes don't arrive, or committing an offence themselves.

Even if the banknotes do arrive, they are worthless and any shop or bank which receives one is obliged to confiscate it and notify the authorities.

Simon Phillips, managing director at No1 Currency said: 'It is worrying to see organised criminals advertising so brazenly on social media. 

'Their slick ads offer a dangerous fantasy - the chance to buy supposedly realistic looking banknotes for a fraction of their face value.

'The reality is that modern banknotes are bristling with security features - from holograms and precision printing to distinctive metal inserts placed within the multi-layer polymer construction.

'The social media companies must do more to tackle such blatantly fraudulent activity. If criminals are able to pay to advertise their services so openly, it suggests there is something seriously rotten in the approvals process at these tech giants.'

Tech companies have come under increased scrutiny for the role they play in how criminals target their victims.

Our sister title Money Mail has been campaigning for the Government to force tech companies to step in and stop the scammers .

Last year, we revealed a new recruitment scam circulating on Whatsapp and how Facebook Marketplace had become a hotbed of scammers .

While big tech firms, including Meta, eBay, Google and Amazon, have signed a voluntary 'Online Fraud Charter', scams are still rife online.

A spokesman for Meta told This Is Money: 'Fraudulent activity is not allowed on our platforms and we remove this content as soon as it is identified. 

'We are continually investing in new technologies to tackle this industry-wide issue, and encourage people to report activity like this to us and the police, so we can take action.'

Top tips for spotting fake bank notes 

Simon Phillips, of No1 Currency, gives his four top tips for spotting counterfeit bank notes and ensuring you have the real thing for your holiday.

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Banknotes and Coin

The South African Reserve Bank (SARB) released the upgraded Mandela banknotes and fourth decimal coin series on 3 May 2023. 

The upgraded banknotes and fourth decimal coin series have new designs and enhanced security features that utilise the latest technological advancements to protect the integrity of our currency and maintain public trust.

The upgraded banknotes continue to pay homage to South Africa's first democratically elected president, Nelson Mandela, with his portrait featured on the front of all five denominations while the Big 5 animals are depicted as a family. The fourth decimal coin series is based on the theme of deep ecology, which celebrates the interconnectedness of humans and other living organisms as an integral part of the environment.

The upgraded banknotes and fourth decimal coin series will co-circulate with the current series and retain the same value per denomination.

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When the Export-Import Bank closed up, US companies saw global sales plummet

In recent years, export credit agencies have become a focal point of debate among policymakers and economists. These agencies, established by governments to facilitate international trade by providing assistance to companies, have come under scrutiny from critics who argue they waste taxpayer money by predominantly benefiting large corporations that don’t face financial constraints.

However, a new study coauthored by Chenzi Xu , an assistant professor of finance at Stanford Graduate School of Business, challenges this narrative. Xu is a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR).

Analyzing the impact of the temporary shutdown of the Export-Import Bank of the United States (EXIM) between 2015 and 2019, Xu and her coauthors find that companies relying on its support experienced a significant downturn in exports and saw their global sales plummet an average of 10 percent relative to similar firms.

This shock also led to a permanent reduction in EXIM-supported companies’ investments and employee headcount, underscoring the importance of trade financing. However, the study, published by the National Bureau of Economic Research , found no real difference in these companies’ return on assets. This finding challenges the prevailing notion that export credit simply bolsters profits without having a real impact on companies’ operations.

“Some people take a very dim view of export credit agencies, thinking that companies don’t need this support, considering it as corporate charity or a handout. However, our paper argues that these credit facilities serve a very important economic purpose,” Xu says.

Governments around the world have set up export credit agencies to help companies get financing for international trade when private sources are difficult to access. These agencies are ubiquitous in both advanced economies and developing countries. However, there’s disagreement about how much they help.

Proponents say they boost exports, create jobs, and help the domestic economy grow by filling gaps in private lending. Critics argue that they cause economic distortions, such as misallocating resources by extending taxpayer-funded support to companies that otherwise would be unable to export.

However, this notion is challenged by the new findings from Xu, Adrien Matray, an acting assistant professor of finance at Stanford GSB, and their colleagues Poorya Kabir and Karsten Müller of the National University of Singapore. Their paper shows that the decrease in global sales resulting from the shutdown of EXIM from 2015 to 2019 was mainly seen in firms with high returns to capital investment. This suggests that the agency wasn’t channeling resources to less-efficient firms.

Additionally, the paper finds that the EXIM closure caused a much bigger drop in sales — about five times higher — for companies that were already highly productive, based on their marginal revenue product of capital. This implies that the shutdown made it harder for money to flow to the most productive companies. And that could mean that cutting export credit subsidies might increase, rather than decrease, the misallocation of resources.

“Export credit agencies play a massive role in supporting companies facing financial obstacles in financing their exports. So there are huge potential economic consequences of curtailing export credit subsidies, as some critics have called for,” Xu says.

Closed for business

The EXIM closed in 2015 after the U.S. Congress did not renew its charter. As a result, the value of the agency’s financial support to firms declined by almost 85 percent between 2014 and 2019. It wasn’t until late 2019 that the agency’s charter was renewed and support for exports resumed.

One of the key questions Xu and Matray’s study raises is why companies benefiting from EXIM support were so significantly affected by its closure. Their research attributes this impact to these firms being financially constrained, perhaps because they were already highly leveraged, making it difficult to replace EXIM support with private sector support.

Xu and her colleagues also find that EXIM might be filling a lending gap left by the private financial sector. “As an agency of the U.S. government, it can have better loan loss recovery than a private bank might,” she explains. “This makes it profitable for EXIM to finance trade even to very risky countries in which the private sector is unlikely to operate.”

The study’s implications go beyond the immediate impacts of EXIM’s closure, raising broader questions about the effectiveness of export credit subsidies and their role in supporting productivity and trade. The findings suggest that policymakers must carefully consider the ramifications of reducing support for export credit agencies, as it could lead to inefficiencies in resource allocation and, ultimately, hinder economic growth.

“Our paper also found that  industries  that depended more on EXIM support experienced a bigger decline in exports — implying that the decrease in exports at the firm level also had broader effects on entire industries, rather than just shifting market share among U.S. companies in favor of those supported by EXIM,” Xu says.

“Can governments boost exports by providing targeted trade financing?” Xu, Matray, and their coauthors ask. “The results in this paper… suggest that the answer is yes.”

A version of this story was originally published May 3, 2024 by Stanford Graduate School of Business Insights.

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Are Markups Driving the Ups and Downs of Inflation?

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FRBSF Economic Letter 2024-12 | May 13, 2024

How much impact have price markups for goods and services had on the recent surge and the subsequent decline of inflation? Since 2021, markups have risen substantially in a few industries such as motor vehicles and petroleum. However, aggregate markups—which are more relevant for overall inflation—have generally remained flat, in line with previous economic recoveries over the past three decades. These patterns suggest that markup fluctuations have not been a main driver of the ups and downs of inflation during the post-pandemic recovery.

In the recovery from the pandemic, U.S. inflation surged to a peak of over 7% in June 2022 and has since declined to 2.7% in March 2024, as measured by the 12-month change in the personal consumption expenditures (PCE) price index. What factors have been driving the ups and downs of inflation? Production costs are traditionally considered a main contributor, particularly costs stemming from fluctuations in demand for and supply of goods and services. As demand for their products rises, companies need to hire more workers and buy more intermediate goods, pushing up production costs. Supply chain disruptions can also push up the cost of production. Firms may pass on all or part of the cost increases to consumers by raising prices. Thus, an important theoretical linkage runs from cost increases to inflation. Likewise, decreases in costs should lead to disinflation.

Labor costs are an important factor of production costs and are often useful for gauging inflationary pressures. However, during the post-pandemic surge in inflation, nominal wages rose more slowly than prices, such that real labor costs were falling until early 2023. By contrast, disruptions to global supply chains pushed up intermediate goods costs, contributing to the surge in inflation (see, for example, Liu and Nguyen 2023). However, supply chains have more direct impacts on goods inflation than on services inflation, which also rose substantially.

In this Economic Letter , we consider another factor that might drive inflation fluctuations: changes in firms’ pricing power and markups. An increase in pricing power would be reflected in price-cost markups, leading to higher inflation; likewise, a decline in pricing power and markups could alleviate inflation pressures. We use industry-level measures of markups to trace their evolving impact on inflation during the current expansion. We find that markups rose substantially in some sectors, such as the motor vehicles industry. However, the aggregate markup across all sectors of the economy, which is more relevant for inflation, has stayed essentially flat during the post-pandemic recovery. This is broadly in line with patterns during previous business cycle recoveries. Overall, our analysis suggests that fluctuations in markups were not a main driver of the post-pandemic surge in inflation, nor of the recent disinflation that started in mid-2022.

Potential drivers of inflation: Production costs and markups

To support households and businesses during the pandemic, the Federal Reserve lowered the federal funds rate target to essentially zero, and the federal government provided large fiscal transfers and increased unemployment benefits. These policies boosted demand for goods and services, especially as the economy recovered from the depth of the pandemic.

The increase in overall demand, combined with supply shortages, boosted the costs of production, contributing to the surge in inflation during the post-pandemic recovery. Although labor costs account for a large part of firms’ total production costs, real labor costs were falling between early 2021 and mid-2022 such that the increases in prices outpaced those in nominal wages. This makes it unlikely that labor costs were driving the surge in inflation.

Instead, we focus on another potential alternative driver of inflation that resulted from firms’ ability to adjust prices, known as pricing power. As demand for goods surged early in the post-pandemic recovery, companies may have had a greater ability to raise their prices above their production costs, a gap known as markups. Following a sharp drop in spending at the height of the pandemic, people may have become eager to resume normal spending patterns and hence more tolerant to price increases than in the past. In fact, growth of nonfinancial corporate profits accelerated in the early part of the recovery (see Figure 1), suggesting that companies had increased pricing power. Some studies have pointed to the strong growth in nonfinancial corporate profits in 2021 as evidence that increased markups have contributed to inflation (see, for example, Weber and Wasmer 2023). However, the figure also shows that growth in corporate profits is typically volatile. Corporate profits tend to rise in the early stages of economic recoveries. Data for the current recovery show that the increase in corporate profits is not particularly pronounced compared with previous recoveries.

Figure 1 Profit growth for nonfinancial businesses

More importantly, corporate profits are an imperfect measure of a firm’s pricing power because several other factors can drive changes in profitability. For instance, much of the recent rise in corporate profits can be attributed to lower business taxes and higher subsidies from pandemic-related government support, as well as lower net interest payments due to monetary policy accommodation (Pallazzo 2023).

Instead of relying on profits as a measure of pricing power, we construct direct measures of markups based on standard economic models. Theory suggests that companies set prices as a markup over variable production costs, and that markup can be inferred from the share of a firm’s revenue spent on a given variable production factor, such as labor or intermediate goods. Over the period of data we use, we assume that the specific proportion of a company’s production costs going toward inputs does not change. If the share of a firm’s revenue used for inputs falls, it would imply a rise in the firm’s price-cost margin or markup. In our main analysis, we use industry-level data from the Bureau of Economic Analysis (BEA) to compute markups based on the share of revenue spent on intermediate inputs. Our results are similar if we instead use the share of revenue going toward labor costs.

We compare the evolution of markups to that of prices, as measured by the PCE price index, since the recovery from the pandemic. In constructing this price index, the BEA takes into account changes in product characteristics (for instance, size) that could otherwise bias the inflation measure by comparing the prices of inherently different products over time. Similarly, based upon standard economic theory, our markup measure implicitly captures changes in those characteristics (see, for example, Aghion et al. 2023).

The post-pandemic evolution of markups

We examine the evolution of markups in each industry since the third quarter of 2020, the start of the post-pandemic recovery. Figure 2 shows that some sectors, such as the motor vehicles and petroleum industries, experienced large cumulative increases in markups during the recovery. Markups also rose substantially in general merchandise, such as department stores, and for other services, such as repair and maintenance, personal care, and laundry services. Since the start of the expansion, markups in those industries rose by over 10%—comparable in size to the cumulative increases over the same period in the core PCE price index, which excludes volatile food and energy components. However, the surge in inflation through June 2022 was broad based, with prices also rising substantially outside of these sectors. Thus, understanding the importance of markups for driving inflation requires a macroeconomic perspective that examines the evolution of aggregate markups across all sectors of the economy.

Figure 2 Cumulative changes in markups for salient industries

The role of aggregate markups in the economy

To assess how much markup changes contribute to movements in inflation more broadly, we use our industry-level measurements to calculate an aggregate markup at the macroeconomic level. We aggregate the cumulative changes in industry markups, applying two different weighting methods, as displayed in Figure 3. In the first method (green line), we match our industry categories to the spending categories in the core PCE price index for ease of comparison; we then use the PCE weights for each category to compute the aggregate markup. Alternatively, we use each industry’s cost weights to compute the aggregate markup (blue line). Regardless of the weighting method, Figure 3 shows that aggregate markups have stayed essentially flat since the start of the recovery, while the core PCE price index (gray line) rose by more than 10%. Thus, changes in markups are not likely to be the main driver of inflation during the recovery, which aligns with results from Glover, Mustre-del-Río, and von Ende-Becker (2023) and Hornstein (2023) using different methodologies or data. Markups also have not played much of a role in the slowing of inflation since the summer of 2022.

Figure 3 Cumulative changes in aggregate markups and prices

Moreover, the path of aggregate markups over the past three years is not unusual compared with previous recoveries. Figure 4 shows the cumulative changes in aggregate markups since the start of the current recovery (dark blue line), alongside aggregate markups following the 1991 (green line), 2001 (yellow line), and 2008 (light blue line) recessions. Aggregate markups have stayed roughly constant throughout all four recoveries.

Figure 4 Cumulative changes of aggregate markups in recoveries

Firms’ pricing power may change over time, resulting in markup fluctuations. In this Letter , we examine whether increases in markups played an important role during the inflation surge between early 2021 and mid-2022 and if declines in markups have contributed to disinflation since then. Using industry-level data, we show that markups did rise substantially in a few important sectors, such as motor vehicles and petroleum products. However, aggregate markups—the more relevant measure for overall inflation—have stayed essentially flat since the start of the recovery. As such, rising markups have not been a main driver of the recent surge and subsequent decline in inflation during the current recovery.

Aghion, Philippe, Antonin Bergeaud, Timo Boppart, Peter J. Klenow, and Huiyu Li. 2023. “A Theory of Falling Growth and Rising Rents.”  Review of Economic Studies  90(6), pp.2,675-2,702.

Glover, Andrew, José Mustre-del-Río, and Alice von Ende-Becker. 2023. “ How Much Have Record Corporate Profits Contributed to Recent Inflation? ” FRB Kansas City Economic Review 108(1).

Hornstein, Andreas. 2023. “ Profits and Inflation in the Time of Covid .” FRB Richmond Economic Brief 23-38 (November).

Liu, Zheng, and Thuy Lan Nguyen. 2023. “ Global Supply Chain Pressures and U.S. Inflation .” FRBSF Economic Letter 2023-14 (June 20).

Palazzo, Berardino. 2023. “ Corporate Profits in the Aftermath of COVID-19 .” FEDS Notes , Federal Reserve Board of Governors, September 8.

Weber, Isabella M. and Evan Wasner. 2023. “Sellers’ Inflation, Profits and Conflict: Why Can Large Firms Hike Prices in an Emergency?” Review of Keynesian Economics 11(2), pp. 183-213.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

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With inflation soaring, Argentina will start printing 10,000 peso notes

File - A worker counts money at a grocery store in Buenos Aires, Argentina, Nov. 21, 2023. Prices have surged so dramatically that the government has multiplied the size of its biggest banknote in circulation to 10,000 peso note, five times the value of the previous biggest bill, according to the central bank on May 8, 2024, and the new bill is expected circulate in June. (AP Photo/Natacha Pisarenko, File)

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BUENOS AIRES, Argentina (AP) — Prices in Argentina have surged so dramatically in recent months that the government has multiplied the size of its biggest bank note in circulation by five — to 10,000 pesos, worth about $10.

The central bank announcement Tuesday promised to lighten the load for many Argentines who must carry around giant bags — occasionally, suitcases — stuffed with cash for simple transactions. Argentina’s annual inflation rate reached 287% in March, among the highest in the world.

The new denomination note — five times the value of the previous biggest bill — is expected to hit the streets next month in a bid to “facilitate transactions between users,” the central bank said. The 10,000 peso note is worth $11 at the country’s official exchange rate and $9 at the black market exchange rate.

Across Argentina, hard currency — specifically, the country’s ubiquitous 1,000-peso notes — remains the most popular way to pay for things. When first printed in 2017, the 1,000-peso note was worth $58 on the black market. Now, it’s worth a dollar.

Given the instability unleashed by Argentina’s worst financial crisis in two decades, vendors prefer old-fashioned cash payments for big purchases and offer steep discounts to incentivize paper bills over electronic transfers.

Argentina’s libertarian President Javier Milei , who took office last December, campaigned on a promise to tame inflation and stabilize the local currency by reversing the policies of past left-leaning governments that printed money to finance public spending.

But in the meantime, his harsh austerity drive has pushed prices up to levels in the U.S. and Europe, adding to the economic woes of ordinary Argentines . A massive nationwide strike, the latest in a series of protests , is planned for Thursday.

Even as annual inflation remains high, Milei cites a gradual slowdown in Argentina’s monthly inflation rate since last December to insist his plan is working. Confident consumer prices can continue creeping downward, policymakers lowered the central bank’s key interest rate three times last month.

The new 10,000 peso notes feature small artistic portraits of Manuel Belgrano, a founding father of Argentina, and María Remedios del Valle, a Black Argentine woman and army captain who gained fame fighting the country’s War of Independence.

Argentina’s central bank said it would introduce an even bigger bill — a 20,000-peso note — later this year.