research projects on insurance

120+ Creative Insurance Project Topics For Students In 2023

Insurance plays a pivotal role in safeguarding individuals and businesses from unforeseen risks, offering a protective financial cushion in times of need. However, in academia, students often delve into the intricacies of insurance through dedicated projects. These insurance projects serve as essential tools for grasping the nuances of risk management, finance, and more.

But what exactly is an insurance project? It’s a comprehensive exploration of various facets of insurance, shedding light on principles, practices, and real-world applications. The importance of such projects for students cannot be overstated, as they provide a practical understanding of a vital industry.

In this blog, we’ll delve into the world of insurance projects, discussing their types, 120+ creative insurance project topics for students in 2023, essential components of a quality project, and the challenges and opportunities that await. Stay tuned with us to explore the dynamic realm of “insurance project topics.

What Is An Insurance Project?

Table of Contents

An insurance project is like a special school assignment where you learn about how insurance works. It’s a bit like when you study history or science, but here, you’re studying how insurance helps people and businesses. You look at things like how insurance companies calculate prices, why people need insurance, and how they pay when something goes wrong.

In an insurance project, you might also investigate different types of insurance, like car insurance or health insurance. It’s a bit like exploring different flavors of ice cream – each type of insurance is unique, and you get to understand how they work. Overall, an insurance project is like a learning adventure where you become a detective, figuring out how to protect people from bad things that can happen in life.

Types Of Insurance Projects

Insurance projects encompass a wide range of endeavors designed to provide individuals and organizations with financial protection against various risks and uncertainties. These projects are essential in mitigating potential losses and promoting economic stability. Here are some common types of insurance projects:

1. Life Insurance Projects

Projects involving life insurance entail drafting policies that, in the case of the policyholder’s passing, give beneficiaries financial support. These policies can be term life insurance, whole life insurance, or universal life insurance, each with distinct features and benefits.

2. Health Insurance Projects

Health insurance projects focus on developing and managing policies that cover medical expenses, ensuring individuals have access to quality healthcare. These projects often include collaborations with healthcare providers and regulatory compliance.

3. Property and Casualty Insurance Projects

Property and casualty insurance projects deal with protecting individuals and businesses against property damage, liability, and legal expenses. Examples include homeowners’ insurance, automobile insurance, and liability insurance.

4. Commercial Insurance Projects

Commercial insurance projects cater to the unique needs of businesses, providing coverage for property, liability, and other specialized risks. This category includes commercial property insurance, business liability insurance, and workers’ compensation insurance .

5. Reinsurance Projects

Reinsurance projects involve insurance companies seeking coverage for their own risk exposure by transferring a portion of their policies to other insurers. This helps manage their financial stability and protect against catastrophic losses.

Importance Of Insurance Projects For Students

Here are some importance of insurance projects for students: 

1. Practical Learning

Insurance projects provide students with hands-on experience, helping them understand how insurance works in the real world. It’s like learning to ride a bike by actually riding one – students get to see insurance principles in action, making their knowledge more practical.

2. Risk Management Skills

These projects teach students about handling risks wisely. Just like a superhero who knows how to protect people from danger, students learn to protect businesses and individuals from financial risks by studying insurance.

3. Financial Literacy

Insurance projects help students become money-savvy. They learn how insurance can save people from big financial problems and how to manage their own money better in the future. Additionally, the advent of bizinsure Insurance Fintech is revolutionizing how these financial challenges are approached and resolved.

4. Problem-Solving Abilities

Students develop problem-solving skills when they explore different insurance scenarios. It’s like a puzzle where they figure out how to make things right when something goes wrong.

5. Career Opportunities

Understanding insurance through projects can open doors to various job opportunities in the insurance industry. It’s like having a map that shows them different paths to take in their future careers, making it an important step for their professional growth.

In this section we will discuss 120+ creative insurance project topics for students in 2023:

Life Insurance Project Topics

• Actuarial Analysis of Life Insurance Policies
• Consumer Behavior and Life Insurance Choices
• The Impact of Medical Underwriting on Life Insurance Premiums
• Assessing the Role of Life Insurance in Estate Planning
• Evaluating the Tax Implications of Life Insurance Products
• Analysis of Mortality and Morbidity Trends in Life Insurance
• Innovation in Life Insurance Products: Trends and Implications
• Market Penetration of Life Insurance in Developing Countries
• Customer Retention Strategies in the Life Insurance Industry
• Risk Management in Life Insurance Companies

Health Insurance Project Topics

• Comparative Analysis of Health Insurance Plans
• The Affordable Care Act’s Effect on Health Insurance Markets
• Health Insurance Fraud Detection and Prevention
• Telemedicine and Its Role in Health Insurance
• Mental Health Coverage in Health Insurance Plans
• Health Insurance and Healthcare Utilization Patterns
• Long-Term Health Insurance: Needs and Challenges
• International Perspectives on Health Insurance Systems
• Health Insurance and Healthcare Disparities
• Health Insurance and the Aging Population

Property and Casualty Insurance Project Topics

• Catastrophic Risk Modeling in Property and Casualty Insurance
• Claims Management and Fraud Detection in P&C Insurance
• Data Analytics and Predictive Modeling in Property Insurance
• Automobile Insurance Pricing and Risk Assessment
• Climate Change’s Effect on Property Insurance
• Cybersecurity Risks and P&C Insurance
• Liability Insurance for Businesses: Coverage and Trends
• Reinsurance Strategies in Property and Casualty Insurance
• Telematics and Usage-Based Insurance in the Auto Industry
• Emerging Risks in Property and Casualty Insurance

Commercial Insurance Project Topics

• Risk Assessment in Commercial Property Insurance
• Business Interruption Insurance: Claims and Controversies
• Workers’ Compensation Insurance and Occupational Health
• Liability Insurance for Small Businesses
• Insurance Needs of the Hospitality Industry
• Supply Chain Risk Management and Commercial Insurance
• Insurtech Innovations in Commercial Insurance
• Key Considerations for Commercial Property Valuation
• Business Continuity Planning and Commercial Insurance
• Commercial Fleet Insurance and Vehicle Safety

Reinsurance Project Topics

• Reinsurance Market Dynamics and Trends
• Risk Management Strategies for Reinsurance Companies
• Catastrophe Bonds and Alternative Risk Transfer
• Reinsurance Underwriting and Risk Selection
• Retrocession and Its Role in Reinsurance
• Reinsurance Pricing Models and Actuarial Methods
• The Impact of Regulatory Changes on the Reinsurance Industry
• Reinsurance and Solvency II Compliance
• Mergers and Acquisitions in the Reinsurance Sector
• Role of Reinsurance in Managing Emerging Risks

Specialty Insurance Project Topics

• Specialty Insurance Products and Market Niche
• Environmental Liability Insurance: Challenges and Opportunities
• Kidnap and Ransom Insurance: Trends and Case Studies
• Fine Art and Collectibles Insurance: Valuation and Coverage
• Space Insurance and Coverage for Satellite Launches
• Event Cancellation Insurance in the Entertainment Industry
• Specialized Insurance Needs in the Energy Sector
• Identity Theft and Cyber Insurance Coverage
• Political Risk Insurance in International Trade
• Unique Risks and Innovative Solutions in Specialty Insurance

Crop and Agriculture Insurance Project Topics

• Crop Yield Risk Assessment and Insurance
• Weather Index Insurance in Agriculture
• Impact of Climate Change on Crop Insurance
• Government Subsidies and Crop Insurance Participation
• Crop Insurance and Sustainable Agriculture Practices
• Challenges in Insuring Specialty Crops
• Livestock Insurance and Disease Outbreaks
• Precision Agriculture and Its Role in Crop Insurance
• Agricultural Insurance and Food Security
• Risk Management in Organic Farming and Agriculture Insurance

Marine and Aviation Insurance Project Topics

• Maritime Insurance: Cargo and Hull Coverage
• Marine Pollution and Liability Insurance
• Aviation Insurance: Covering Aircraft and Airlines
• Terrorism Risk and Aviation Insurance
• Space Exploration and Insurance for Spacecraft
• Drone Insurance and Regulatory Challenges
• Maritime Piracy and Kidnap Insurance for Seafarers
• International Shipping Risks and Marine Insurance
• Aviation Underwriting and Risk Management
• Environmental Liability in Maritime and Aviation Insurance

Environmental and Pollution Insurance Project Topics

• Environmental Liability Insurance in Industrial Settings
• Pollution Cleanup Costs and Insurance Coverage
• Insurance Solutions for Environmental Contractors
• Emerging Contaminants and Their Insurance Implications
• Climate Change and Its Impact on Environmental Insurance
• Regulatory Compliance and Environmental Liability Coverage
• Environmental Insurance Market Trends and Challenges
• Assessing and Managing Liability in Brownfield Sites
• Green Building and Insurance for Sustainable Construction
• Case Studies of Environmental Insurance Claims

Personal Insurance Project Topics

• Homeowners Insurance: Coverage and Risk Assessment
• Auto Insurance: Pricing, Coverage, and Discounts
• Life Events and Personal Insurance Needs
• Umbrella Insurance Policies: Coverage and Benefits
• Personal Liability Insurance for Individuals
• Renters Insurance: Importance and Coverage Options
• Personal Property Insurance and Valuation
• Pet Insurance: Trends and Coverage
• Travel Insurance and Its Role in Vacation Planning
• Insurance for High-Value Personal Assets and Collectibles

Legal Expenses Insurance Project Topics

• Legal Expenses Insurance: Overview and Market Analysis
• Legal Aid and Access to Justice through Insurance
• Personal Legal Expenses Insurance: Benefits and Coverage
• Litigation Funding and Legal Expenses Insurance
• Insurance for Business Legal Expenses and Risk Management
• Regulatory Compliance and Legal Expenses Insurance
• International Perspectives on Legal Protection Insurance
• Cyber Liability and Legal Expenses Coverage
• Legal Expenses Insurance and Dispute Resolution
• Ethics and Legal Expenses Insurance in the Legal Profession

Long-Term Care Insurance Project Topics

• Long-Term Care Insurance: Market Trends and Challenges
• The Growing Population’s Requirement for Long-Term Care Insurance
• Medicaid and Long-Term Care: Interplay and Coverage Gaps
• Hybrid Long-Term Care Insurance Products
• Actuarial Considerations in Long-Term Care Insurance Pricing
• Alzheimer’s Disease and Long-Term Care Planning
• Regulatory Oversight of Long-Term Care Insurance
• Family Dynamics and Long-Term Care Decision-Making
• Home Care vs. Nursing Home Care: Insurance Implications
• Claims Management in Long-Term Care Insurance

Cyber Insurance Project Topics

• Cybersecurity Risks and the Need for Cyber Insurance
• Data Breach Insurance: Coverage and Risk Assessment
• Actuarial Models for Pricing Cyber Insurance
• Cyber Risk Management and Insurance Solutions for Businesses
• Regulatory Compliance and Cyber Insurance
• Ransomware Attacks and Cyber Insurance Claims
• Cyber Insurance Underwriting and Risk Selection
• Emerging Cyber Threats and Insurance Implications
• Cyber Insurance for Small and Medium-Sized Enterprises
• Reinsurance Strategies in the Cyber Insurance Market
• MBA HR Project Topics
• Health Related Research Topics

Essential Things That Must Be Present In A Good Insurance Project Topics

Here are some essential things that must be present in a good insurance project topic:

1. Relevance

A good insurance project topic must be relevant to real-life situations. Just like a story that makes sense, the topic should address current insurance issues or needs, making it useful and meaningful.

2. Clear Focus

The topic should be like a flashlight in a dark room, helping students see their way. It must have a clear and specific focus so that students can explore it thoroughly without getting lost.

3. Research Opportunities

An ideal project topic should provide room for research. It’s like a treasure hunt, where students can dig deep and find valuable information to enrich their project.

4. Practical Application

The chosen topic should be something that can be applied practically. It’s like learning to cook a new recipe; students should be able to take what they’ve learned and use it to solve insurance-related problems.

5. Educational Value

Lastly, the topic should be educational, helping students learn new things about insurance. It’s like a book that’s not just interesting but also teaches valuable lessons, ensuring students gain knowledge and insights from their project.

Challenges Face By Students In Insurance Projects

Undertaking insurance projects can be an enriching experience for students, but it’s not without its challenges. These projects often require a deep knowledge of complex financial concepts, extensive research, and critical thinking. Here are some common challenges students may face:

• Complex Terminology: Students may struggle with the jargon and technical language commonly used in insurance, making it hard to grasp the finer details.
• Data Collection: Gathering accurate and relevant data for analysis can be time-consuming and demanding, especially when dealing with real-world insurance scenarios.
• Mathematical Calculations : Insurance projects often involve intricate mathematical calculations , and errors can harm project accuracy.
• Industry Knowledge: A lack of familiarity with the insurance industry and its evolving trends can pose a significant challenge in producing well-informed projects.
• Resource Constraints: Limited access to resources like industry experts or databases can hinder in-depth research.
• Analytical Skills : Interpreting and analyzing data can be challenging, especially for students with limited experience in statistics and data analysis.
• Time Management: Balancing project work with other academic commitments can be daunting, as insurance projects demand thorough research and analysis.

Insurance project topics have shed light on the significance of these projects for students. We’ve discovered that insurance projects offer invaluable practical learning experiences, imparting essential skills like risk management and financial literacy. They provide doors to various opportunities and act as a stepping stone toward a future career in the insurance sector.

Furthermore, we’ve highlighted the crucial attributes of a good insurance project topic: relevance, focus, research potential, practical applicability, and educational value. With a repertoire of 120+ creative project ideas for 2023, students now have a roadmap to embark on their insurance learning journey. In the ever-evolving world of insurance, these projects empower students to navigate and contribute to this critical field.

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The impact of artificial intelligence along the insurance value chain and on the insurability of risks

• Open access
• Published: 08 February 2021
• Volume 47 , pages 205–241, ( 2022 )

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• Martin Eling 1 ,
• Davide Nuessle 1 &
• Julian Staubli 1  

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Based on a data set of 91 papers and 22 industry studies, we analyse the impact of artificial intelligence on the insurance sector using Porter’s ( 1985 ) value chain and Berliner’s ( 1982 ) insurability criteria. Additionally, we present future research directions, from both the academic and practitioner points of view. The results illustrate that both cost efficiencies and new revenue streams can be realised, as the insurance business model will shift from loss compensation to loss prediction and prevention. Moreover, we identify two possible developments with respect to the insurability of risks. The first is that the application of artificial intelligence by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry’s most inherent problems, namely asymmetric information. The second development is that artificial intelligence might change the risk landscape significantly by transforming some risks from low-severity/high-frequency to high-severity/low-frequency. This requires insurance companies to rethink traditional insurance coverage and design adequate insurance products.

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Motivation and aim of the paper

There is a growing consensus on the potential of artificial intelligence to transform modern economies and societies (Abrardi et al. 2019 ; Bolton et al. 2018 ; Boyd and Holton 2017 ; Makridakis 2017 ) by enabling computer systems to carry out numerous tasks and activities that are typically considered to require human intelligence, thereby significantly improving efficiency and efficacy. At the same time, there is a controversial debate over the risks and limitations of artificial intelligence. Footnote 1

The progress and popularity Footnote 2 of artificial intelligence results from the combination of two developments that enable its productive use. The first is that artificial intelligence has matured, thanks to recent advancements in machine learning and deep learning algorithms (Abrardi et al. 2019 ). The second is that the availability of big data combined with the rapidly increasing computation power of modern information technology systems accelerates the development and increases the accuracy of artificial intelligence applications (Allam and Dhunny 2019 ; Thrall et al. 2018 ). As a result, considerable progress has been made in the capabilities of artificial intelligence in the last few years. Footnote 3 There is a wide range of real-world use cases across industries. Among these are pattern and anomaly detection (e.g. for fraud mitigation, see e.g. Ahmed et al. 2016 ), speech recognition and natural language generation (e.g. for the development of chatbots, see e.g. Dale 2016 ), recommendation engines (e.g. for automated product suggestions, see e.g. Marchand and Marx 2020 ), image recognition (e.g. for improved public safety, see e.g. Zhang et al. 2016 ), and automated decision-making systems (e.g. for robo-advice, see e.g. Faloon and Scherer 2017 ).

While some industries such as banking, Footnote 4 healthcare, Footnote 5 manufacturing Footnote 6 and software development Footnote 7 have been investing in artificial intelligence for years (Bughin et al. 2017 ), industry studies note that the insurance sector is lagging behind in the worldwide and intersectoral artificial intelligence movements (Rangwala et al. 2020 ; Deloitte 2017 ). Nevertheless, it is likely that artificial intelligence will have a broad impact along the insurance value chain, from underwriting and claims management over distribution and customer service to asset management. Consequently, insurance executives must understand the new technologies that will contribute to this change and how artificial intelligence can help organisations create innovative products, glean valuable insights from new data sources, streamline business processes and improve customer service.

The intention of this paper is to support practitioners in understanding the potential benefits associated with artificial intelligence applications and to motivate academics to study this multifaceted, controversial and heavily under-researched topic. Towards this end, we establish a database of papers and industry studies on the use of artificial intelligence in the insurance sector and systematically evaluate the impact of artificial intelligence along Porter’s ( 1985 ) value chain and on Berliner’s ( 1982 ) insurability criteria. Based on the review results, we derive potential future work from practitioners’ and researchers’ perspectives. In this way, we provide practitioners and academics with a high-level overview of the most important research topics and promote future work in this field. To structure our discussion, the paper is organised into three core steps:

Description of artificial intelligence applications that will influence the insurance sector.

Analysis of the impact of these applications along the insurance value chain and derivation of benefits for insurance companies as well as insurance customers.

Deduction of the consequences for the insurability of risks.

The remainder of this paper is structured as follows. We begin with a short description of our research methodology ( Research approach ). Then, the literature on the three core research topics is reviewed ( Survey of existing knowledge on artificial intelligence in insurance ). Finally, the results are summarised and potential areas of future work from both the industry and research perspectives are discussed ( Summary and derivation of potential future work ).

Research approach

Literature review.

The literature review consists of a structured search and identification process based on vom Brocke et al. ( 2009 ) and Webster and Watson ( 2002 ). We review the academic literature by using a search string that includes several keywords in combination with ‘insurance’ or ‘insurer’. The selection of keywords is based on Niu et al. ( 2016 ), who conducted a keyword analysis drawing on 20,715 articles on artificial intelligence published between 1990 and 2014. The keywords include terms for disciplines, subdisciplines, techniques and application areas of artificial intelligence. However, as some of the keywords are vague (e.g. ‘management’, ‘identification’, ‘optimisation’) and research on artificial intelligence has developed over the past five years, we have amended the keywords accordingly. Footnote 8 The keywords used in the literature review are summarised in Table  1 .

The literature search is conducted in the journal databases EBSCOhost (Business Source Ultimate, Computer Source and EconLit) and ABI/INFORM Collection. These databases were chosen because of their focus on business- and economic-related topics and because they include the relevant insurance-related journals. Footnote 9 The search process (1) was restricted to papers published from 2000 to June 2020, (2) focused on scholarly (peer-reviewed) publications and (3) searched for keywords in the abstract. The search process is displayed in Fig.  1 .

Literature search process based on vom Brocke et al. ( 2009 ) and Webster and Watson ( 2002 )

In total, exactly 400 publications were found in the two databases. After their examination, 68 papers were identified as relevant for this literature review. A backward search, Footnote 10 as proposed by Webster and Watson ( 2002 ), was then conducted, where an additional 2056 sources were screened, and 23 relevant papers and 13 industry studies were found. Footnote 11 Another nine industry studies were identified by performing a regular Google search with the defined keywords. Based upon this selection process, a database of 91 papers and 22 industry studies (see Appendix B in the electronic supplementary material) is developed and the main results are extracted. The 91 papers consist of 86 journal articles and five trade journal articles. Based on their content, the papers were assigned to the respective stage in the insurance value chain (see Table B1 in the electronic supplementary material). Footnote 12 Industry studies could not be mapped to a single step of the value chain because they discuss the impact of artificial intelligence on the entire insurance industry and across the value chain, so for them a separate list (see Table B2 in the electronic supplementary material) has been created.

Conceptual frameworks: value chain and insurability criteria

Following Eling and Lehmann ( 2018 ), we use two conceptual frameworks to illustrate our results. The first, Porter’s ( 1985 ) value chain, distinguishes a firm’s primary from supporting activities in delivering a product or service; because Porter’s ( 1985 ) value chain was not formulated for a specific industry and was intended to be a rather general concept, we adapt it using the insurance-specific value chain by Rahlfs ( 2007 ) (see Fig.  2 ).

Insurance-specific value chain based on Porter ( 1985 ) and Rahlfs ( 2007 )

We then analyse Berliner’s ( 1982 ) insurability criteria, a frequently used and comprehensive approach for differentiating insurable and uninsurable risks. Nine insurability criteria cover five actuarial, two market-specific and two societal aspects of insurability (see Table  2 ). This approach has been used, for example, by Biener et al. ( 2015 ) to analyse the insurability of cyber risks, by Charpentier ( 2007 ) to scrutinise the insurability of climate risks and by Gehrke ( 2014 ) to evaluate agricultural production risks. We refer to Berliner ( 1982 , 1985 ) and Biener et al. ( 2015 ) for further details on the criteria.

Survey of existing knowledge on artificial intelligence in insurance

The digitalisation Footnote 13 of the insurance industry is already quite advanced and has gone far beyond the transition from analogue to digital information processing (Stoeckli et al. 2018 ). Eling and Lehmann ( 2018 ) describe digitalisation as ‘the integration of the analogue and digital worlds with new technologies that enhance customer interaction, data availability, and business processes’. Digital transformation is also driven by InsurTechs, Footnote 14 which have emerged in the last decade (Riikkinen et al. 2018 ). New technologies affecting the insurance industry include cloud computing, Footnote 15 telematics, the Internet of Things (IoT), Footnote 16 mobile phones, blockchain technology, Footnote 17 artificial intelligence and predictive modelling (Cappiello 2020 ). Digitalisation has already had a considerable impact along the insurance value chain and will continue to do so as new technologies emerge and mature (Eling and Lehmann 2018 ). Footnote 18 Key changes comprise enhanced process efficiency, improved underwriting and product development, reshaped customer interactions and distribution strategies and new business models (Albrecher et al. 2019 ). Bohnert et al. ( 2019 ) show in their study that digitalisation activities have a significantly positive impact on the business performance of insurance companies. Footnote 19

At the beginning of the digitalisation wave, the main focus was on online and digital distribution channels (Garven 2002 ) and their impact on insurance agents (Eastman et al. 2002a , 2002b ), customers (Kaiser 2002 ) and competition (Brown and Goolsbee 2002 ). In the ensuing years, the ubiquity of mobile and interconnected devices exponentially increased the availability of customer data. Footnote 20 The extensive amount of available data has opened up new opportunities for insurance companies to apply innovative technologies for their benefit. For this reason, access to the vast amount of customer data forms the basis for numerous artificial intelligence applications and can be considered a precondition for the implementation of artificial intelligence by insurance companies.

What is artificial intelligence and which technologies will influence the insurance industry?

The first developments concerning artificial intelligence began more than 60 years ago with the construction of the first ‘thinking machines’: computer systems with human-like intelligence equalling, and at some point, exceeding that of human beings (Baum et al. 2011 ; Lake et al. 2016 ). To test a machine’s ability to exhibit intelligent, humanoid behaviour, the Turing test was invented (Turing 1950 ). Footnote 21 The first definitions of the term ‘artificial intelligence’ date from this time. However, as a result of the various conceptions and the rather vague nature of (human) intelligence, there is no widely accepted definition of artificial intelligence but rather a multitude of coexisting definitions (Wang 2019 ; see also Bhatnagar et al. 2018 ; Monett and Lewis 2018 ). Footnote 22

McCarthy ( 2007 ), who played a leading role in coining the term artificial intelligence in 1955, describes it as the science and engineering of manufacturing intelligent machines. Barr and Feigenbaum ( 1981 ) describe artificial intelligence in more detail as the part of computer science concerned with designing intelligent computer systems, systems that exhibit characteristics associated with intelligence in human behaviour such as understanding written and spoken language, learning, reasoning or solving problems. A survey by Monett and Lewis ( 2018 ) asked professionals and experts worldwide to comment on hundreds of definitions of artificial intelligence. The most accepted definition was Wang’s ( 2008 ): ‘The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real-time, open to unexpected tasks, and learn from experience. This working definition interprets intelligence as a form of relative rationality.’ For the purpose of this paper, we base our understanding of artificial intelligence on Kelley et al.’s ( 2018 ) more comprehensive description of artificial intelligence as ‘a computer system that can sense its environment, comprehend, learn, and take action from what it is learning’. Footnote 23

The premise of artificial intelligence applications is to train computer systems with large amounts of data obtained through IoT and other big data sources to recognise patterns and apply their learned abilities to new data sets. The three types of artificial intelligence—categorised by their degree of intelligence Footnote 24 —are narrow, general and super (Kaplan and Haenlein 2019 ). Artificial narrow intelligence systems are trained to perform very specific physical or cognitive tasks; they operate within a limited context and a predefined range. In contrast, artificial general intelligence works on broader problem areas and has the capacity to assess its surroundings and give emotionally-driven responses comparable to those of humans. Artificial super intelligence systems, which exhibit the potential to outperform humans across a wide range of disciplines, have not yet been developed and are very likely still decades away (Jajal 2018 ). Table  3 summarises the three types of artificial intelligence.

Compared to classical rule-based systems, where data is strictly processed as initially defined through programming rules, artificial intelligence algorithms can learn and improve themselves independently based on past experiences (Kreutzer and Sirrenberg 2020 ). The method used to train these algorithms and thus realise artificial intelligence is machine learning. It consists of four types of learning: supervised, semi-supervised, unsupervised and reinforcement (Gentsch 2018 ; Kreutzer and Sirrenberg 2020 ). The most common type of machine learning is supervised learning, which requires humans to define each element of the input and output data. The algorithm is then trained to find the connection between the input and output variables of the data set, so that the answers are derived as precisely as possible. The second most common type is unsupervised learning, which does not include predefined output variables. The aim of the algorithms is to identify patterns and structures among the input variables independently. Semi-supervised and reinforcement learning are rather rare, and we refer to Kreutzer and Sirrenberg ( 2020 ) for their explanation.

Over the past few years, deep learning has gained increasing attention in artificial intelligence research. Deep learning, Footnote 25 which was not widely accepted as a viable form of artificial intelligence until 2012 (Krizhevsky et al. 2017 ), is a subset of (unsupervised) machine learning. While conventional machine learning techniques are limited in processing raw data, deep learning allows the processing of data from a wider range of data sources and requires less human effort to pre-process data (LeCun et al. 2015 ). Due to the increasing volume and complexity of data and the rapid development of modern computing, deep learning has recently become increasingly popular (Yu et al. 2018 ). Footnote 26 In the last decade, deep learning has made significant progress in numerous fields (Yuan et al. 2019 ) such as speech recognition (see e.g. Graves et al. 2013 ), image classification (see e.g. Rawat and Wang 2017 ; Yu et al. 2017 ; He et al. 2016 ), language translation (see e.g. Young et al. 2018 ), object recognition (see e.g. Krizhevsky et al. 2017 ) and detection (see e.g. Ren et al. 2017 and Redmon and Farhadi 2017 ), and has outperformed other machine learning techniques. Even though the predictive accuracy of artificial neural networks has greatly improved, the networks’ internal logic often remains inexplicable and incomprehensible due to their inherent complexity (Knight 2017 ; Castelvecchi 2016 ). Footnote 27 Most of the discussions among insurance practitioners with regard to applying artificial intelligence for parts of their value creation still focus on conventional machine-learning-enabled applications, as deep learning is still in the development phase and cannot yet be reliably deployed and implemented across a wide range of tasks (Panetta 2018 ). However, deep learning is expected to have a significant impact on the insurance industry as it requires very little human engineering to benefit from the increasing amount of available data and computation power.

To date, there is no common description of the different application fields of artificial intelligence. Some experts have created IT-related categories such as ‘machine learning’, ‘modelling’ or ‘problem-solving’ (see e.g. Görz et al. 2013 ; Russell and Norvig 2012 ). However, Kreutzer and Sirrenberg ( 2020 ) see machine learning and deep learning not as independent fields of application but rather as the basis of artificial intelligence usage. They define natural language processing, natural image processing/computer vision, expert systems and robotics as the four major application fields of artificial intelligence. They further note that many artificial intelligence applications, such as autonomous vehicles, represent a mixed form of these applications.

Table  4 summarises insurance-relevant artificial intelligence applications based on a systematic assessment of all the 91 papers and 22 industry studies (see Appendix B in the electronic supplementary material), explains them and maps specific industry use cases. The applications cover the full process from accessing to processing data and from evaluating to deploying data for enhanced decision-making or process optimisation. Many high-level applications across the value chain, such as automated claims management, combine multiple artificial intelligence applications such as text analysis and natural language processing, image and video analysis, as well as pattern and anomaly detection.

The use cases show that most applications in the insurance industry, ranging from the analysis of images of customers through the use of algorithms for the estimation of contractual terms for life insurance policies to the optimisation of fraud detection, aim to realise artificial narrow intelligence (weak AI) as they solve very specific tasks. In light of today’s insurance markets, insurance companies are thus more interested in applications of artificial narrow intelligence than in mimicking human intelligence (strong AI). The impact of more human-like artificial general intelligence on the insurance industry remains unknown as the technology is not yet fully understood and developed. For now, insurance companies should focus on the implementation of artificial narrow intelligence while monitoring the technological developments of artificial general intelligence. Most applications focus on specific areas of the value chain and are used for customer and operations efficacy: scenarios where the computational advantage, speed and accuracy of artificial intelligence are mainly levered. Using artificial intelligence to generate new insights or to reveal previously unknown results is more difficult to realise from a technological point of view (Deloitte 2017 ). Today’s most prominent use cases in this category are telematics-enabled usage-based insurance contracts in the health, motor and property and casualty segment. Footnote 28 Start-ups such as Oscar Footnote 29 use machine learning algorithms, for instance, to analyse claim data and make inferences about the frequency of certain activities and procedures doctors perform. Based on the results, Oscar is able to identify experts and specialists in certain treatments to refer policyholders to the most suitable hospital. As another example, Lemonade Footnote 30 is changing several links in the traditional insurance value chain by replacing brokers, underwriting agents, service employees and fraud detection experts with artificial intelligence systems.

What is the impact of artificial intelligence along the insurance value chain?

We continue our systematic assessment of all 91 papers and 22 industry studies to summarise the impact of artificial intelligence applications along the insurance-specific value chain (Table  5 ). Footnote 31

There are three principal categories of change initiated by artificial intelligence systems (see Eling and Lehmann 2018 ). The first is the way in which insurance companies interact with their customers (e.g. sales, customer service) is being transformed. While customer services traditionally required personal interaction with an agent, broker or bank for customer queries and product information due to a lack of alternatives, the information available has improved significantly over the internet and/or via chatbots. Some products can even be purchased online via chatbots without any personal interaction. This enables insurance companies to deploy human sales and customer service agents more effectively as chatbots take over some of their tasks. The insureds benefit through the availability of customer service and product information at any time and at a higher speed. Further along the value chain, digital technologies, such as apps, offer assistance and support claims reporting. Especially important is the use of artificial intelligence in risk reduction and prevention, for example, by proactive customer outreach in a risky situation. This enables the insurance industry to evolve from a ‘detect and repair’ to a ‘predict and prevent’ mode (Kelley et al. 2018 ). If implemented, this might lead to a completely new business model: preventing losses through a comprehensive risk management solution rather than compensating losses (The Geneva Association 2018 ). Such a development has the potential to decrease overall losses, which would not only benefit insurance companies and insureds, but also economic welfare.

The second change is the automation of business processes (e.g. processing of contracts, reporting of claims) and decisions (e.g. underwriting, claim settlement, product offerings). While transaction-intensive industries such as health insurance are already using background processing, the use of big data and artificial intelligence will stimulate a further wave of automation. The biggest benefits of automation for insurance companies are potential cost savings. Furthermore, a higher accuracy for administrative repetitive tasks can be achieved by eliminating human errors and skilled employees will have more time to concentrate on truly value-adding tasks. Automation in the reporting and settlement of claims will accelerate business processes, leading to greater customer satisfaction. As artificial intelligence applications can process and analyse large amounts of data generated by telematic devices, social networks or other internet sources (e.g. customer feedback, pictures, videos) in, for example, the underwriting process, insureds may have to answer fewer questions, which increases their satisfaction and hence has a positive impact on customer retention. One major challenge with the use of big data and artificial intelligence in this context is the accompanying ethical and legal issues. These include discussions about the extent to which insurers are allowed to use all of the generated data for decision making, how long the data has to be retained and which actions insurers must take to protect the data against, for instance, cybercrime (Hussain and Prieto 2016 ).

While the first two categories of change are closely related to the impact of artificial intelligence along the insurance value chain, as discussed in Table  5 , the third category includes fundamental changes in insurance markets, which have not been discussed so far. The development of artificial intelligence will not only create new insurance markets and new risks but also cause certain existing markets to disappear (The Geneva Association 2018 ). One obvious example is autonomous driving, which changes the nature of liability in the automotive industry. Who is liable in case of an accident: the passenger, the car manufacturer or the software developer of the artificial intelligence algorithms? This development questions whether traditional car insurance, as we know it today, will still exist in the future. Cyber risks arising from the use of artificial intelligence technologies are also generating new market opportunities, with some industry studies predicting that cyber risk insurance might become the largest non-life segment in 2032 (e.g. KPMG 2018 ).

In addition to the impact of artificial intelligence in each single stage of the insurance value chain, the combination of these changes will have profound implications for the entire insurance landscape. The increasing prevalence of digital technologies in society causes traditional industry borders to blur. The resulting ecosystems will significantly influence the future of the insurance industry. Footnote 32 Ecosystems can be understood as ‘an interconnected set of services that allow users to fulfil a variety of needs in one integrated experience’ (Catlin et al. 2018 ). The most relevant ecosystems for the insurance industry include the mobility, home and health ecosystems. These ecosystems offer insurance companies the opportunity to not only enter new revenue streams by reconsidering their traditional roles in the economy but also to integrate their insurance products into seamless customer journeys (Lorenz et al. 2020 ). While insurance companies currently have a passive and limited relationship with insureds, the emergence of ecosystems might cause significant changes in the way they interact with customers and how they distribute their products and services. In the mobility ecosystem, for example, insurance companies face the opportunity to expand their services to areas such as the purchase of vehicles, parking, traffic management and car sharing (Catlin et al. 2018 ). The potential benefits of ecosystems for insurance companies further include increased customer retention, improved loss prevention to reduce claims and lower distribution costs (Lorenz et al. 2020 ).

Table  6 summarises the major benefits of artificial intelligence applications for insurance companies and customers along the value chain. The results in Table  6 are derived from our findings in Table  5 . As previously mentioned, the reduction of insurance costs—whether through decreasing loss payments or transaction costs—is beneficial both for the shareholders of insurance companies and for the insureds. Lower insurance costs will increase the insurer’s profitability, leading to a higher shareholder value, but will also reduce premiums if passed on to the insureds (which can be assumed in competitive markets). Regardless of which case occurs (depending on the competitive situation), the reduction of insurance costs ultimately leads to an increase in economic welfare.

How does artificial intelligence influence the insurability of risks?

Table  7 summarises the expected influence of artificial intelligence on the insurability of risks structured along Berliner’s ( 1982 ) insurability criteria. The results in Table  7 are deduced from our results in Tables  4 and 5 . The assessment distinguishes between (a) the application of artificial intelligence by insurance companies themselves and (b) the changes in the risk landscape triggered by artificial intelligence. The results show that the application of artificial intelligence by insurers does not compromise but rather improves the insurability of risks. The only exception is criterion 8, public policy, which remains unclear as the application of artificial intelligence implies increased transparency of policyholders’ sensitive data, which potentially raises ethical and moral questions. However, the change in the risk landscape as a result of the increasing implementation of artificial intelligence poses many challenges to the insurability of risks and raises numerous questions for all insurability criteria.

The heterogenous results underline that a clear distinction between the application of artificial intelligence by insurers and the changes in risks triggered by artificial intelligence is of utmost importance. For this reason, we divide our subsequent discussion into these two categories.

Application of artificial intelligence by insurers

In light of today’s insurance markets, the application of artificial intelligence by insurance companies shows three major effects in the context of the insurability of risks. The increasing availability of detailed risk-relevant information about policyholders through historical and real-time data sets will change traditional actuarial risk assessment and pricing models. The granular analysis of texts, images and videos from internal and external databases, as well as from connected devices (i.e. telematics devices and health wearables), allows insurance companies to more accurately estimate and predict loss probabilities and loss amounts on an individual level. This enables insurance companies to distinguish good and bad risks more precisely and thus reduce adverse selection. Additionally, it might even give those with bad risks an incentive to increase loss prevention efforts or to change their behaviour; hence, it also reduces moral hazard (e.g. usage-based insurance products). It further allows insurance companies to form small and homogenous risk groups with accurate and adaptive premium pricing schemes for each policyholder as risk-relevant behaviour, including prevention effort, is transparent and directly measurable. Consequently, bad risks will pay a higher and good risks a lower premium. This, however, raises questions related to the affordability of premiums for bad risks, which potentially contradicts insurance criterion 6.

In addition, the acquisition, processing and storage of sensitive customer data by insurance companies must be compliant with data privacy and security laws, as well as with moral and ethical considerations. Sensitive customer data is the basis of numerous artificial intelligence applications and it is thus crucial for insurance companies to ensure compliance with legal frameworks (e.g. GDPR). For this reason, responsible data management can be considered a precondition for a successful implementation of artificial intelligence. Another critical precondition is public policy, especially social and ethical considerations. The problem of discrimination caused by artificial intelligence was recently demonstrated by Amazon’s recruiting algorithm; its rating of candidates for software developer jobs showed bias against women. Footnote 33 Hence, a transparent and anti-discriminatory application of artificial intelligence is crucial to gain the willingness of insureds to entrust their sensitive data to an insurer.

Finally, new risks become insurable with the implementation of artificial intelligence. Automated and continuous underwriting reduces transaction costs and will enable the extension of On-Demand insurance for various assets. Examples could include additional insurance coverage for personal belongings against theft or damage, Footnote 34 travel insurance and by-the-mile car insurance. Insurance coverage can thus be purchased for a wide range of low-severity risks for the time the asset is actually used and ‘at risk’. Additionally, loss assessments of an insured event can be significantly accelerated by artificial intelligence, which accelerates the claims management process and the corresponding payments. Thereby, the most severe risks, such as crop insurance against natural disasters, can be covered by insurance companies. Footnote 35 Consequently, artificial intelligence applications by insurers push the boundaries of insurability as several low- and high-severity risks become insurable.

Changes in risks triggered by artificial intelligence

The insurance market of the future will be shaped by numerous everyday artificial intelligence applications. For example, self-driving vehicles and healthcare with proactive, real-time and data-driven analysis of health status will emerge. This development will have a significant impact on the risk landscape and has two major implications for the insurability of risks. Artificial intelligence applications have the potential to transform the nature of loss events. Given the example of autonomous driving, the total number of accidents is likely to be considerably reduced, implying much lower loss probabilities (contradicts insurability criterion 4). However, a breakdown of the underlying artificial intelligence system or a hacking attack can cause a cascading series of accidents resulting in a considerable increase in the maximum possible loss (contradicts insurability criterion 2). Hence, loss events are not independent due to increasing connectedness (contradicts insurability criterion 1) and the shift from high-frequency/low-severity to low-frequency/high-severity risks. Similar concerns are discussed by Biener et al. ( 2015 ), who concluded that accumulation risk Footnote 36 poses a major hurdle to the insurability of cyber risks. A potential way to reduce accumulation risk and ensure sufficient independency of loss events could be the diversification of applied artificial intelligence systems, which would improve insurability. High-severity risks also require very high cover limits and premium payments, which could contradict insurability criteria 6 and 7. Hence, insurance companies are challenged to revise traditional insurance coverage and design innovative insurance products.

In addition, ethical and legal aspects of artificial intelligence arise whenever algorithms have to make difficult decisions (e.g. whether a malfunctioning autonomous vehicle should strike a child or a group of adults, i.e. Foot’s trolley problem, see e.g. Nyholm and Smids 2016 ), thereby raising liability issues (see e.g. Jarrahi 2018 ). Autonomous vehicles can demonstrate the potential safety problems related to artificial intelligence applications in everyday life. A fatal collision between an artificial-intelligence-controlled Uber vehicle and a pedestrian in Arizona in 2018 exemplifies this statement (see e.g. National Transportation Safety Board 2019 ). Furthermore, the data processed in artificial intelligence algorithms and the obtained insights raise questions regarding data security and protection (i.e. data access and usage). The need to regulate companies that develop and use artificial intelligence is evident. The use of algorithms ranges from autonomous vehicles to decision support systems in the health sector, as well as in artificial-intelligence-powered weapon systems. National and international institutions are responsible for developing guidelines for a fair and appropriate handling of artificial intelligence applications. However, the demands for transparency, non-discrimination and fairness clearly show the limits of the application of artificial intelligence as some principal dilemmas cannot be resolved. For example, the way in which machine learning arrives at the respective conclusions has never been—and due to the technical peculiarities will never be—completely transparent. Another ethical dilemma arises in the context of the fairness of artificial intelligence. An activity that a company or public authority considers fair might not have to be fair from the perspective of the consumer or citizen.

Despite all these concerns, the enormous potential of artificial intelligence must not be ignored. There still has not been an appropriately broad discussion of the limitations and concerns that reflects the relevance of the topic. However, as the technology is already being implemented and will have a profound impact on our everyday life, urgent action is required.

Summary and derivation of potential future work

We provide an overview of various artificial intelligence applications within the insurance industry and analyse their impact along the insurance-specific value chain based on Porter ( 1985 ) and in light of the insurability criteria developed by Berliner ( 1982 ). Table  8 summarises the results of the three core topics discussed in the previous section. Based on these results, we identify potential areas of future work from both an academic and practical perspective.

The numerous entry points illustrate that artificial intelligence has the potential to change many activities across the insurance value chain. The main opportunities for value generation will evolve around process automation (leading to cost savings and thus margin expansion) and the use of additional customer insights for entering new revenue streams, acquiring new customers and more personalised interactions with existing customers (leading to revenue growth). Today, the adoption of artificial intelligence within insurance markets is in its earliest stages and the academic research on the implications of artificial intelligence on the insurance business model is still limited. However, the topic is attracting increasing attention and interest from practitioners worldwide, as illustrated by the rapidly growing and generously funded InsurTech sector. Footnote 37 The present paper helps practitioners navigate their organisations to take full advantage of the benefits of artificial intelligence, and motivates academics to pave the way for a successful adoption of artificial intelligence by answering important research questions and running empirical analyses that go beyond the scope of this paper.

Today’s artificial narrow intelligence systems are trained to perform only very specific tasks (e.g. a chess computer cannot play poker). Of course, weak artificial intelligence is not the ultimate goal of the tech companies that are investing billions of dollars in the development of the technology. They try to develop artificial general intelligence systems that are capable of abstract and creative thinking and making judgements under conditions of uncertainty (Uj 2018 ). Without knowing if the development of these artificial intelligence systems is actually possible, experts expect the first system to be ready in the next 10 to 30 years (Uj 2018 ). Given this vague time horizon, insurance managers, policymakers and regulators need to focus on the technology that is in place now (i.e. artificial narrow intelligence or weak AI). At the same time, it is important to track technological development and to continuously update potential management and regulatory frameworks in this dynamic field of research and practice.

From a scientific point of view, the changes in asymmetry of information and the associated economic welfare effects are intriguing. Linked to this is the question regarding the value of data from the customer’s and provider’s points of view. Thus, in the face of a latent fatalism in dealing with data, it is not quite clear what privacy is worth from the customer perspective (Biener et al. 2020 ). Positive effects of artificial intelligence applications on economic welfare can also be found in the field of prevention at the collective level when it comes to better understanding large amounts of data and using them for the benefit of customers. On an individual level, however, welfare effects are not negligible, because there may be both winners and losers in digital monitoring by artificial intelligence systems.

Several shortcomings of this paper might motivate future research. One is the generalisation of the analysis to the entire insurance sector. This offers both practitioners and academics a sense of the scope of the topic, but it lacks accuracy and applicability, because insurance segments and product lines are heterogeneous. Consequently, a detailed analysis of artificial intelligence on single steps of the value chain for each major type of insurance or the evaluation of upcoming artificial intelligence trends (e.g. neural networks that pave the way to the development of artificial general intelligence) on the insurance sector could be interesting. Moreover, we show some future scenarios where insurers could become enablers of social good, like increased longevity and improved public safety. It would be interesting to analyse the role of the insurance sector in combatting significant societal challenges in health and elderly care. For example, a steadily increasing number of elderly people are living with chronic diseases and require personal care services. However, the number of care professionals and doctors is not keeping pace with the growth of this population. Research can include the role of artificial intelligence applications, such as health nanobots, tracking devices and chatbots, to support health and elderly care.

A second shortcoming is the analysis of insurability criteria, which are somewhat vague because of missing empirical evidence. Consequently, our assessment serves as an indicator of whether or not single criteria are likely to be contradicted by the implementation of artificial intelligence. So far, no academic studies have directly analysed the effects of artificial intelligence on important actuarial metrics such as adverse selection, moral hazard and risk pooling or market criteria. From a practitioner's perspective, the question is still open as to whether better risk-based calculation of premiums will lead to lower combined ratios as both losses and the collected premiums are expected to move in tandem. It might lead to better insurance products with higher customer value, but it is not entirely clear if artificial intelligence is Pareto-optimal in the sense that every client will profit from the increasing use of artificial intelligence. From a general welfare point of view, we would expect to profit if artificial intelligence reduced the number of claims, but there is no overall assessment yet. The paper also highlights the importance of societal insurability criteria, but a detailed analysis goes beyond the scope of this paper as several external factors are likely to be relevant.

Further thoughts have led us to the following open questions: What is the role of insurance companies when technology firms dominate access to data? How will insurance companies react if data and privacy regulation become more restrictive and prohibit the use of policyholders’ personal information? Will self-driving vehicles and health nanobots transform risks to the extent that the traditional idea of insurance comes into question? Will the public perception and brand image of insurance companies suffer as people become uncomfortable with constant surveillance? Will increased transparency and usage-based pricing lead to less solidarity in the context of social insurance? Will this lead to social unrest if high-risk policyholders can no longer afford insurance? Or will good risks try to opt out of traditional insurance pools with cross-subsidisation across risk classes (e.g. in social security schemes)? These questions will have a direct impact on insurance corporations over the next few years, so it is important for insurance executives to start thinking about these scenarios today.

In 2014, Stephen Hawking stated that ‘success in creating effective AI [artificial intelligence], could be the biggest event in the history of our civilization. Or the worst. We just don’t know’ (Kharpal 2017 ).

Figure A1 in Appendix A in the electronic supplementary material illustrates the exponential growth in the academic interest of artificial intelligence by showing the development of published articles on the subject in Web of Science from 1980 to 2019.

In 2016, the programme AlphaGo defeated a human professional player for the first time in the full-sized game Go (Silver et al. 2016 ). Only 14 years earlier, this was believed to be impossible due to the complexity of the game compared to, for example, chess (Müller 2002 ).

See e.g. Jakšič and Marinč ( 2019 ) on the role of artificial intelligence in the banking sector.

See e.g. Jiang et al. ( 2017 ) and Patel et al. ( 2009 ) for an overview of artificial intelligence in medicine.

See e.g. Li et al. ( 2017 ) and Lee et al. ( 2018 ) for applications of artificial intelligence in manufacturing.

See e.g. Kothari ( 2019 ) for an overview of artificial intelligence applications in software engineering processes.

See Martínez-Plumed et al. ( 2018 ) for a discussion of the keywords provided by Niu et al. ( 2016 ).

For example, The Journal of Finance , American Economic Review , Journal of Risk and Insurance , Insurance: Mathematics and Economics , The Geneva Papers on Risk and Insurance—Issues and Practice , The Geneva Risk and Insurance Review , Journal of Insurance Regulation and Risk Management & Insurance Review .

A backward search is the process of screening the references of the initially identified papers.

Moreover, all working papers from the annual meetings of the American Risk and Insurance Association (ARIA; for 2012 to 2019), the 2015 World Risk and Insurance Economics Congress and the European Group of Risk and Insurance Economists conferences 2011, 2012, 2013 and 2016 are examined. Surprisingly, no additional sources were identified through this examination, emphasising that there is still a lack of research on these topics in the risk and insurance community.

The focus of research on artificial intelligence in the insurance sector is on claim management and underwriting and pricing. A quantitative examination of the number of identified papers per stage of the value chain shows that 38% of the 91 papers address the application of artificial intelligence in claim management, while 26% assess the usage of artificial intelligence in underwriting and pricing. The other value chain stages have a percentage share below 10%, indicating that the application of artificial intelligence in these areas is still heavily under-researched (see Table C1 in Appendix C in the electronic supplementary material for more details).

Digitalisation is often used interchangeably with digitisation (see e.g. BarNir et al. 2003 ). However, a clear distinction should be made between the two. While digitisation is the technical process of converting analogue data into digital forms, digitalisation describes the adoption of digital technologies in various contexts (Legner et al. 2017 ). These two developments lead to digital transformation, which triggers profound changes in business and society (Majchrzak et al. 2016 ; Vial 2019 ).

InsurTech encompasses the emerging technologies, innovative business models, applications, processes and products that might transform the traditional insurance sector (International Association of Insurance Supervisors 2017 ). For an overview of the InsurTech landscape see e.g. Braun and Schreiber ( 2017 ).

See e.g. Akhusama and Moturi ( 2016 ) who analysed cloud computing uses in terms of productivity applications, business applications, infrastructure on-demand, finance applications, core business applications and databases in insurance companies in Kenya.

The Internet of Things can be defined as a ‘collection of smart devices that interact on a collaborative basis to fulfil a common goal’ (Sicari et al. 2015 ).

See e.g. Gatteschi et al. ( 2018 ) for a discussion on several blockchain use cases in the insurance sector.

See also The Geneva Association ( 2018 ) for a discussion of the impact of digital technologies on insurance and the role of insurance in an increasingly digitised economy.

See Bohnert et al. ( 2019 ) for an analysis of the relationship between the expression of a digital agenda in annual reports and the business performance of 41 publicly-traded European insurance companies for the time period 2007 to 2017.

The collected data include traditional, structured, transactional data as well as contemporary, unstructured, behavioural data, commonly referred to as ‘Big Data’ and characterised by its volume, velocity, variety, veracity and value (Erevelles et al. 2016 ; Lycett 2013 ). Big Data might, for example, simplify the detection of insurance fraud (Bologa et al. 2013 ).

Turing ( 1950 ) proposed that a machine has reached intelligent behaviour once a human evaluator cannot tell whether or not he or she was engaged in natural conversation with another human or with a machine.

See Wang ( 2019 ) for a discussion of the difficulties in defining artificial intelligence.

See Appendix D in the electronic supplementary material for a summary of definitions of artificial intelligence.

There are many definitions of intelligence. Grewal ( 2014 ) defines intelligence as ‘a general mental ability for reasoning, problem solving, and learning’. The term intelligence generally refers to the ability to acquire and apply different skills and knowledge to solve a problem (Neisser et al. 1996 ).

See e.g. LeCun et al. ( 2015 ). In a deep learning neural network, a digitised input (e.g. an image or speech) proceeds through multiple layers (typically from 5 to 1000) of connected ‘neurons’, of which each responds to a different feature of the input and an output is ultimately provided (Topol 2019 ). Neural networks are defined as ‘neuron-like processing units that collectively perform complex computations’ (Lake et al. 2016 ). As the name suggests, this artificial intelligence method originates in neuroscience. Initially, research on artificial intelligence was intertwined with neuroscience and psychology (Churchland and Sejnowski 1988 ; Marblestone et al. 2016 ). The first attempts to construct artificial neural networks that could compute logical functions were made in the 1940s (McCulloch and Pitts 1943 ). There are manifold types of deep learning neural network algorithms. For reviews see e.g. Goodfellow et al. ( 2016 ) and Yu et al. ( 2018 ).

Unlike classical neural networks, deep learning applies more hidden layers, resulting in superior processing of complex data with manifold structures (Goodfellow et al. 2016 ).

Due to these opaque decision-making systems, deep learning is often described as a ‘Black Box System’ (Guidotti et al. 2018 ).

See e.g. Ayuso et al. ( 2019 ) for a discussion on improving automobile insurance ratemaking using telematics by incorporating mileage and driver behaviour data.

https://www.hioscar.com/ .

https://www.lemonade.com/ .

In Appendix E (see electronic supplementary material), we combine Tables  4 and 5 into a ‘value chain and technology matrix’.

Catlin et al. ( 2018 ) expect the emergence of 12 major ecosystems which will account for approximately USD 60 trillion in revenues by 2025. This highlights the significant impact of ecosystems on the global economy.

See e.g. Dastin (2018).

Insuring certain assets against theft with an On-Demand insurance product could be especially attractive during a short vacation.

An example is the RIICE project, which provides satellite-based crop production monitoring. The assessment of an insured event can be completed more quickly and at relatively lower costs than the previous process of loss assessors travelling to the area and assessing the damage on site. See http://www.riice.org/about-riice/about-riice/ .

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Total InsurTech funding volume has soared from USD 869 million in 2014 (94 deals) to over USD 6.3 billion (314 deals) in 2019 (CB Insights 2020 ).

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Eling, M., Nuessle, D. & Staubli, J. The impact of artificial intelligence along the insurance value chain and on the insurability of risks. Geneva Pap Risk Insur Issues Pract 47 , 205–241 (2022). https://doi.org/10.1057/s41288-020-00201-7

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Five steps to improve innovation in the insurance industry

Insurance is not typically considered a bastion of innovation, despite a long track record of creating new and exciting markets around emerging risks and consumer demands. For example, the relatively nascent cyber insurance market is forecast to surpass $22.4 billion by 2026 at an annual growth rate of more than 25 percent in the next five years, according to market research and consulting firm IndustryARC. 1 Cyber insurance market – forecast (2021-2026) , IndustryARC, June 2021, industryarc.com. In reaction to the lockdowns of the COVID-19 pandemic, many insurers rapidly digitalized their customer and agent experience , permanently shifting away from a traditional face-to-face service model. Other carriers are responding to consumer demand for more meaningful interactions with loyalty and gamification programs that promote customer engagement. For instance, South African insurer Discovery’s Vitality loyalty program gives customers points as incentives for practicing healthy habits and good driving behavior, and then grants them access to rewards and benefits.

The C-suite is already taking note of the key role innovation will play in delivering long-term value: data from a 2020 survey show that while executive teams focused on short-term cash management and the welfare of their workforce at the peak of the pandemic, innovation now ranks as one of their top two priorities .

But while the industry as a whole has delivered pockets of innovation, few carriers have pursued innovation in a systemic way. Today, new customer expectations, low interest rates, and new sources of competition (such as leading tech companies, insurtechs, and third-party capital) are putting pressure on carriers to take a more systematic approach. For innovation to deliver sustainable growth, it must be embedded in the company’s growth model and fully integrated across the organization, bringing together cross-functional teams to approach challenges in new ways.

And that’s not easy. Successfully profiting from innovation is a complex, company-wide endeavor, and most insurers have not yet cracked this code—at least not on a consistent basis. In fact, a 2017 survey of life and annuities executives found that only 12 percent believe they have a process that delivers strong product innovation. 2 Marianne Purushotham et al., Understanding the product development process of individual life insurance and annuity companies , Society of Actuaries, 2017, soa.org. And fewer than 30 percent of financial-services executives say they have the expertise, resources, and commitment to successfully pursue new sources of growth.

Fortunately, there are ways to establish and implement cross-cutting practices and processes to structure, organize, and encourage innovation for sustainable growth. Here are five steps for building innovation into the way an organization works, competes, and grows.

Successfully profiting from innovation is a complex, company-wide endeavor, and most insurers have not yet cracked this code—at least not on a consistent basis.

1. Shift resources from core business tasks to breakthrough innovation initiatives

Innovation is not just about creativity and generating unique ideas. It’s about identifying unmet needs and untapped markets and addressing them, sometimes with untested solutions and unproven business models. Yet too many leaders embrace these risks without shifting enough people, assets, and management attention to bring these ideas to life. Put simply, nothing comes from nothing; if a company wants to innovate, it must allocate resources to innovating.

In fact, one of the biggest challenges holding insurers back from innovation is capacity—both physical and human capital  and executive mindshare. Business as usual has continued to be the priority for traditional incumbents, particularly as they have tried to provide stability to customers through the disruption and uncertainty of a global pandemic. Updating existing products, maintaining existing systems, and making incremental changes have taken the lion’s share of insurers’ time, attention, and effort. These short-term initiatives feel safer, particularly given the pressures facing insurers over the past few years. But robust opportunities await insurers  that adjust their valuation criteria and free up capacity for bolder moves.

By reallocating the necessary resources from core business tasks to potentially disruptive initiatives, insurers can rebalance their product portfolios to move away from near-term product improvements and toward potential breakthroughs or new business models—forms of innovation that often hold greater potential to generate sustainable sources of growth and outsize returns.

2. Develop distinct product-development pathways and processes

Different innovation initiatives call for different approaches. For example, most organizations can predict with some certainty the likely gain in gross written premiums or combined ratio from an improvement to an existing coverage or tweak to a core process. This knowledge leads to clarity on the risks and how to mitigate them. This type of innovation is very different from developing a disruptive new product, such as a new life-insurance policy with unprecedented flexibility across living benefits. Disruptive products carry a host of risks—from understanding the market opportunity to communicating the value proposition effectively—and organizations have less clarity around them.

The rapid rise and fall of mutual-aid platforms in Asia illustrate the importance of maintaining a balanced innovation portfolio with different development pathways. In 2019, several companies launched platforms that provide simple access to basic health coverage by radically rethinking product design and customer engagement. Within weeks, the most successful of these platforms, Ant Financial’s Xiang Hu Bao, attracted tens of millions of users, peaking at more than 100 million participants. 3 Georgina Lee, “Ant Financial’s mutual-aid platform Xiang Hu Bao attracts 100 million users, boosts insurers’ sales by 60 per cent in first year,” South China Morning Post , November 27, 2019. But these programs are now winding down. The model encountered both increasing regulatory requirements and adverse selection as young and healthy members dropped out of the program, increasing the costs shared by the remaining participants.

Managing the delivery of an innovation portfolio therefore requires organizations to develop distinct pathways for product development (Exhibit 1). Each pathway has a specific set of characteristics:

• Derisking: This pathway competes with part of the core business and has a high level of ambiguity on the delivery path.
• Derisking and accelerating: With an unknown path to solution, this approach uses technologies and capabilities that are new to the company and requires significant cross–business unit (BU) and change management.
• Accelerating: This pathway has generally known solutions and previous use cases, but its cross-BU implications, infrastructure, and capability to deliver are limited.

One carrier, for example, instituted different development tracks for different types of products:

• New-product development: Totally novel product that the organization has never carried before; not based on an existing product chassis.
• Existing product revamp: Building on an existing product chassis, but developing substantive changes to the product’s features, pricing, and experience to create a distinguishable new product experience
• Simple tweak of current product: Existing products that require very minor updates—such as repricing or adding minor features that already exist in other products

Creating a distinct product-development process for each track allowed the carrier to maintain market share by tweaking existing products while preserving dedicated capacity for new products that have the potential to unlock new markets or value pools.

Risk/return profiles are also used to determine product-development pathways. By analyzing each portfolio product’s economics and its odds of success, insurers can determine which products should be redesigned and which should be coupled with other products. Examples include embedding annuities and other guaranteed-income options in target-date investment funds.

3. Design value propositions that incorporate new approaches to customer engagement and distribution

Innovative value propositions aren’t just about products; they integrate insurance protection and prevention, customer engagement, and distribution and marketing. Historically, carriers have developed new products through actuarial innovation, often adding complexity that appeals more to agents than to customers. Separately, they invest in modernizing and digitalizing their distribution platforms and strengthening new-business and underwriting capabilities.

But carriers need to incorporate all three components in their innovative value propositions to deliver a differentiated experience to customers and distribution partners (Exhibit 2).

Post-COVID-19, a changing customer landscape will continue to encourage carriers to adapt products to deliver a more personalized user experience. This means generating ideas based on unique customer needs and developing a more granular profile of customers to personalize offerings and tailor messaging for even the smallest customer segments.

4. Ensure that innovation is a continuous, integrated process

One common cause of failure is standing up an innovation lab or team without fully integrating it into the business-planning cycle. Innovation teams that are not fully integrated often lack clearly defined, near-term metrics for success. They may not understand how their own success is critical to the success of the overall enterprise and of specific business lines, and they may lack clear links with other parts of the organization to ensure the innovations they develop are implemented and scaled.

By facilitating constant dialogue between innovation and business teams, insurers can foster a common understanding of the market landscape, identify potential opportunities, and realize their aspirations. While the exact cadence may vary for each carrier, it typically includes three main activities throughout the year:

Assess: During this phase, the team conducts a rapid sprint (approximately two to three weeks) to develop a clear market understanding within the strategic-planning cycle and identify key problems to solve (such as customer, distributor, or competitor opportunities). This research will both inform the carrier’s annual strategic planning and determine focus areas for innovation throughout the year. In faster-paced markets, this process may be conducted more frequently.

The goal is to have a robust pipeline that is continually pruned and refilled, with a backlog of ideas that put constant, productive pressure on initiatives currently in development. This pressure helps leaders and teams avoid sunkcost biases and encourages them to weigh the relative value of investing in a current initiative against starting another.

Aspire: In this phase, the team develops a vision for new product opportunities based on user testing with clients and distribution partners, establishing a pipeline of targeted opportunities that can be prioritized and examined before moving into detailed product design.

Initially, a carrier may conduct an accelerated series of workshops to “collide” different ideas. But once the pipeline is established, it should be continually refreshed, and the backlog of ideas should be frequently evaluated and reprioritized. Critically, growth in premium and profit from this portfolio of innovations is incorporated into the overall financial plan and individual executive accountabilities, with the understanding that not all of the ideas will work out, but some must succeed for the organization and leaders to meet their goals in the coming years. We call this overall portfolio goal “the green box”—a quantification of how much growth in revenue or earnings a company’s innovation needs to provide in a given timeframe, translated into cascaded key performance indicators (KPIs) and incentives.

Design, build, and launch: At this point, the team has identified one or more innovation opportunities to bring to market and is ready to proceed with detailed concepts, product design and build (including pricing and filings for insurance products), and go-to-market planning.

Innovation teams should develop a business case for each product or initiative, carefully documenting all assumptions underlying the estimated value. These business cases can, in turn, inform a set of “deal-killing assumptions” that can be tested, refined, and tied to clear milestones and stage gates for each step of the development journey of a given product. For example, proof of concept may involve successful back-testing of a new underwriting approach that results in an increase of expressed interest to purchase the product among at least 20 percent of potential customers. These go/no-go decision points are critical to the team’s ability to reprioritize opportunities quickly, as this phase is typically the costliest and most resource intensive. By getting a clear line of sight into what each innovation needs to succeed, and by testing assumptions early, teams and leaders gain early visibility into which initiatives are likely to succeed or fail so they can refocus efforts and resources accordingly.

5. Pursue more significant product innovations with an accelerator

Building a diverse innovation portfolio and developing a differentiated value proposition require new, cross-functional ways of working. The right innovation operating model will hinge on an insurer’s innovation priorities—from developing capabilities that improve core operations to seeking more disruptive opportunities outside the core offering. External partnerships, strategic M&A, venture-capital models, and traditional R&D can quickly open opportunities to tap into innovative capabilities, products, and processes (Exhibit 3).

But many companies can balance these approaches by standing up an accelerator to pursue transformational innovation and other “step-out” opportunities. Although an accelerator is a separate entity designed to drive product innovation, it must still be focused with clear KPIs and measurable success criteria, including defining the precise amount of innovation-led growth that will help fill gaps in the insurer’s existing growth strategy. Such a unit must also be carefully connected to the existing organization’s centers of strength—distribution, underwriting, and data—so that it can take advantage of those scaled capabilities while maintaining the freedom and space to explore opportunities that are more ambitious and less certain.

For example, after nearly a decade without launching a truly new product, one North American life insurer set up an accelerator and quickly built out a robust innovation portfolio that capitalized on the organization’s product, underwriting, and digital capabilities. The company designed and developed a fundamentally new value proposition for an emerging client segment in less than a year.

Now is the time for insurers to increase the quality, pace, and breadth of innovation. Customer expectations are evolving, challenging carriers to deliver personalized and consumer-centric products. At the same time, the C-suite is recognizing the power of innovation to accelerate the pace of change. For innovation to deliver long-term value, it must extend beyond risks and product offerings and become embedded in a carrier’s DNA through carefully considered priorities, mutually beneficial partnerships, and fully tapped resources.

Kweilin Ellingrud is a senior partner in the Minneapolis office, where Jason Ralph is a partner; Alex Kimura is a partner in the Singapore office; and Brian Quinn is a partner in the Chicago office.

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Insurance Final Year Project Topics and Research Areas

View Project Topics

Insurance final year project topics and research areas encompass a broad spectrum of issues within the insurance industry that students can explore in their final year projects. These topics delve into various aspects of insurance, ranging from risk management and actuarial science to emerging technologies and regulatory challenges. By investigating these areas, students can gain valuable insights into the complexities of the insurance sector and contribute to the advancement of knowledge in the field.

Introduction: Exploring Diverse Topics in Insurance Research

In the final year of an insurance-related academic program, students often undertake research projects that allow them to apply their knowledge and skills to real-world challenges faced by the insurance industry. These projects not only provide an opportunity for students to demonstrate their understanding of core concepts but also encourage them to critically analyze current issues and propose innovative solutions. From studying the impact of climate change on insurance to examining the role of artificial intelligence in underwriting, the possibilities for research topics are vast and varied.

Table of Content

• Risk Management in Insurance
• Actuarial Science and Predictive Modeling
• Emerging Technologies in Insurance
• Regulatory Compliance and Policy Issues
• Customer Behavior and Marketing Strategies

1. Risk Management in Insurance

Research in this area focuses on understanding and managing risks inherent in insurance operations. Topics may include the assessment of catastrophic risk, the development of risk mitigation strategies, and the evaluation of alternative risk transfer mechanisms such as reinsurance and securitization. By investigating risk management practices, students can explore how insurers quantify and price risk, as well as the implications of risk exposure on profitability and solvency.

2. Actuarial Science and Predictive Modeling

Actuarial science plays a crucial role in the insurance industry by providing insights into pricing, reserving, and risk assessment. Projects in this area often involve the application of mathematical models and statistical techniques to analyze insurance data and make informed decisions. Students may explore topics such as mortality and morbidity modeling, reserve estimation methods, and the use of predictive analytics for underwriting and claims management.

3. Emerging Technologies in Insurance

Advancements in technology are transforming the insurance landscape, presenting both opportunities and challenges for insurers. Research topics in this area may include the adoption of blockchain for secure transactions, the use of telematics devices for usage-based insurance, and the development of AI-powered chatbots for customer service. By investigating emerging technologies, students can assess their potential impact on insurance operations, customer experience, and market dynamics.

4. Regulatory Compliance and Policy Issues

Insurance is a highly regulated industry, and compliance with regulatory requirements is essential for insurers to operate ethically and sustainably. Research in this area may focus on understanding regulatory frameworks such as Solvency II, GDPR, and IFRS 17, as well as examining the implications of regulatory changes on insurance business practices. Students may also explore ethical dilemmas and public policy issues related to insurance, such as access to affordable coverage and the role of government intervention.

5. Customer Behavior and Marketing Strategies

Understanding consumer behavior is critical for insurers seeking to attract and retain customers in a competitive marketplace. Research topics in this area may include the analysis of consumer preferences and purchasing decisions, the effectiveness of marketing campaigns and distribution channels, and the impact of digitalization on customer engagement. By studying customer behavior and marketing strategies, students can gain insights into how insurers can better meet the needs of their target market and enhance customer satisfaction and loyalty.

In conclusion, insurance final year project topics and research areas cover a wide range of issues relevant to the insurance industry. From risk management and actuarial science to emerging technologies and regulatory compliance, students have the opportunity to explore diverse facets of insurance and contribute to the advancement of knowledge in the field. By undertaking research projects in these areas, students can develop valuable skills, gain practical experience, and make meaningful contributions to addressing the challenges and opportunities facing the insurance industry.

Innovate to win: Why market research is key to insurance industry success

A changing world demands insurance innovation

Underlying drivers of change are fundamentally transforming the foundations of the insurance industry. New ways to expand insurability and to measure, control, and price risk enable the creation of innovative insurance products and services. Digital platforms disrupt how insurers reach policyholders and potential customers, especially millennials who expect on-demand, high-touch services with delightful user experiences. Technology advances including artificial intelligence and cloud computing improve efficiencies, and with automation, insurers can reduce the cost of a claims journey by as much as 30%. 1 How can insurers leverage these breakthroughs to address unmet consumer demand, successfully launch new insurance products, and drive down costs?

Milliman addresses this question in the “Innovate to win” series. Our first article presented a roadmap to guide you through the entire innovation process. 2 Here, we focus on how you can identify and meet the needs of your customers through market research.

Why do insurers need to conduct market research?

Research into the behavioral economics, marketing, and psychology of insurance products is business-critical for insurers. To sustain profitable growth, insurers must create innovative products and services while improving customer connectivity. The typical insurance company loses 10% to 15% of its customer base every year and the cost of acquiring new customers makes this churn extremely expensive. 3 However, innovation is also expensive and inherently risky. According to Harvard Business School, 95% of the 30,000 new products introduced into the general marketplace each year are failures. 4 With deep risk management expertise and large customer bases, insurers are better positioned to succeed at innovation when compared to other industries.

Successful innovations solve fundamental customer problems in new, better, or more cost-effective ways. Researching customer needs and expectations in the context of your competitive landscape is an integral part of the process. To mitigate risk, all these questions should be researched and answered before launching any innovation into the market:

• What products, services, processes, and ideas are already available in the marketplace?
• What are consumers looking for in this offering and how does it meet their needs?
• What similar products/services do my competitors offer and what are they doing to stay competitive in this market?
• Is this a new offering or different approach to an existing offering?
• What is the potential market size in terms of revenue and profits for this product/service?
• How will we market this offering to consumers?
• Will this offering work as we have designed it?
• Will this product, service, or process disrupt the market, and if so, what impact and value would it have on consumers and the industry?

Market research provides valuable insight into consumer needs and can eliminate misperceptions regarding what potential customers will think about your new product, service, or process. Research can help you clearly define your target market, avoid costly mistakes, and speed product development time. Although market research helps mitigate risk, it does not eliminate it entirely and can be costly. You will need to determine how much time and money you are willing to spend researching the market and if your potential innovation is worth the investment.

What types of market research work best for insurers?

Primary and secondary research are the two most effective ways for insurers to gather information about markets, products, and consumers. Contrary to its name, secondary research is usually conducted first and analyzes existing data. By combining multiple sources of secondary data, you can identify trends and gather useful information at a low cost. You can then use this information to better understand the actions you and/or others have already taken and learn from any mistakes or successes. Secondary research helps maximize future primary research, which is the collection of new data about a specific topic. Certainly, secondary research has value, but it lacks the customization and specificity needed to evaluate larger insurance innovation projects.

When do insurers need to invest in primary research?

A business decision of major consequence requires primary research. Primary research begins with a review of secondary research to efficiently gain direction and insight into the intended study topic. After that, quantitative and/or qualitative methodologies are used to gain further insight into consumer needs, preferences, and behavior. Additional benefits of engaging clients in a research project include strengthening relationships, winning loyalty, and creating new business opportunities.

Quantitative data, typically gathered using surveys, can be represented by usable statistics. Surveys gather a significant amount of data in a relatively short timeframe from a wide range of people, giving you the confidence that the data accurately represents your customer base. This data can provide valuable insight into consumer preferences such as likes and dislikes, satisfaction ratings, and opinions. You can run statistical significance tests to apply results to the population of interest and present the results graphically. Data-driven charts and graphs are an effective way to help stakeholders understand research and convince them to act on the results.

When you need more context regarding your data-- for example, why people feel a certain way about a response-- then qualitative research is the best approach. Sometimes the “why” is critical to exploring a study topic and qualitative research addresses this requirement through focus groups and interviews. These methods enable more in-depth understanding through direct quotes from respondents, the use of themes to bucket responses, and the ability to contextualize answers to understand the “why.” Although qualitative research is valuable, it can be time-consuming and costly when compared to quantitative research. Data is collected from a much smaller sample, so it is difficult to present in an aggregate summary and not statistically significant as being representative of the entire population.

How does primary research advance insurance innovation?

Both types of primary research methods are valuable and can provide insight into the market with different applications and emphasis:

• Quantitative surveys are questionnaires developed specifically for the topic being studied and distributed to a large sample of potential respondents based on specific criteria. Surveys provide a comprehensive view of the market due to a large sample size but are limited in the ability to understand the “why.”
• Qualitative interviews and focus groups provide context by giving participants the opportunity to expand on why they have certain beliefs and opinions and how they feel about the topic of study. In-depth interviews are one-on-one sessions with participants who are selected for their expertise and knowledge in a specified area. Focus groups are moderated discussions of opinions about a specific topic or product. Seven to 10 participants are selected using a screener questionnaire based on specific criteria. The moderator provides the structure, asks the questions, and gives overall direction to guide the discussion.

The most effective product development processes combine quantitative and qualitative research methodologies to refine and validate innovative ideas and prototypes. When you get the results of your research, it is important to have the infrastructure and resources in place to act on those insights. It is also important to note that the results of your research may require you to change your plans because what you previously thought were great ideas were not validated by the market research.

Still, it might be difficult to for your company to adopt new ideas and move forward with your innovation. Administrative systems can slow your company’s product development process and potentially hinder your initiative. Distribution issues can also make or break new product or service delivery. Bottom-line concerns such as low interest rates and the cost of meeting regulatory requirements are key considerations. As a result, many insurers de-emphasize innovative product development initiatives because of resource constraints and development and approval costs. 5

If you are making a big decision regarding an innovation, it is important to dedicate resources to perform in-depth market research. Discovering what your target customers think about your innovation enables you to tailor and refine it before you officially launch it. It is best practice to test multiple variations of your solution with your target market to determine which version resonates most with customers. Research is an opportunity for you to test both the innovation and the messaging you will use when going to market.

If you would like to discuss how customized market research can strengthen the development of your innovative offerings, please contact David Bahlinger or one of the other outstanding professionals at Milliman.

1 McKinsey. (March 2017). Digital disruption in Insurance: Cutting through the noise. Retrieved on May 26, 2020, from https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our%20Insights/Time%20for%20insurance%20companies%20to%20face%20digital%20reality/Digital-disruption-in-Insurance.ashx .

2 Borcan, Ashlee Mouton. Milliman.com. Innovate to win: Insurance industry roadmap to success. March 5, 2020. Retrieved on May 26, 2020, from https://us.milliman.com/en/insight/innovate-to-win-insurance-industry-roadmap-to-success

3 Simpson, Pamela. The Lowdown: Reimagining Research to Recognize Emerging Insurance Industry Trends. (September 19, 2019). Insurance Journal. Retrieved on May 26, 2020, from https://www.insurancejournal.com/blogs/research-trends/2019/09/19/540368.htm .

4 Emmer, Marc. 95 Percent of New Products Fail. Here are six steps to make sure yours don’t. (July 6, 2018). Inc. Retrieved on May 26, 2020, from https://www.inc.com/marc-emmer/95-percent-of-new-products-fail-here-are-6-steps-to-make-sure-yours-dont.html .

5 Society of Actuaries. Understanding the Product Development Process of Life and Annuity Companies. (December 2017). Retrieved on May 26, 2020, from https://www.soa.org/globalassets/assets/files/research/understanding-product-development-report.pdf .

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Research into the behavioral economics, marketing, and psychology of insurance products is business-critical for insurers.

Research Areas

Our objective is to contribute to the advancement of knowledge in the insurance industry by means of rigorous and independent research

This sector faces many research challenges in the current climate, and countless new opportunities are constantly emerging. Big Data analysis presents study possibilities in areas such as integrated risk management or consumer behavior. Areas of great interest for the sector include new risks arising as a result of technological advances, the impact of regulatory change, the digital transformation of the industry, the role of the sector in the economy and systemic risk.

Risk Management

The new focus based on a risk culture means that companies and financial institutions are changing the way they perceive and value their business. The adoption of this focus requires sophisticated control mechanisms which mitigate exposure to risks not assumed by the company.

The interpretation and handling of large databases means that it is now possible to achieve a more detailed and accurate consumer analysis in terms of trends, claim ratios, loyalty etc. which is very useful information for the management of insurance companies and the effective distribution of their products.

Consumer perception and bias

Each individual has their own unique perception of their needs when it comes to protection. The analysis of trends, biases and patterns of behavior allows the product range to be both extended and individualized in a way that is far more efficient for consumer and insurer alike.

The speed of change in the modern world is dizzying, above all in terms of technology and industry. This means that almost on a daily basis we must face new risks which were previously unknown and must be analyzed and assessed. These risks must also be mitigated, almost in their entirety, by various forms of insurance.

The environment conditions established by Solvency II and the adoption of capital criteria based on risk, means that insurers must not only carry out an in-depth review of all of their internal processes, but also develop control strategies which will constantly enhance them.

In order to guarantee that the insurance industry operates in a responsible way, managers, employees and all related parties, must ensure that there is full compliance with regulations. An equally significant aspect is adherence to the individual codes of ethics and policies in every company.

An issue that has generated huge discussion is the provision of public pensions, which will demand profound structural changes in the near future. Private savings will be needed to supplement public cover, so that the developed countries can guarantee economic and social stability.

New products

The development of societies and their economies, along with the global advancement of understanding of risk and its consequences, have all created a need for insurance products, which is ever more sophisticated and tailored to each client in their individual circumstances.

Digital Transformation

The digital technology revolution means that we see the world and our daily lives in a new way. The ease, speed and efficiency that this new technology provides, represents a clear advantage to those companies capable of adapting their processes to the new reality.

Investment strategies

Insurance companies have carried out hard-hitting reviews of their investment strategies in order to adapt to new market conditions. The ongoing situation of low interest rates is a new circumstance which ushers in a range of innovative approaches to investment.

The regulation of the insurance sector protects the interests of customers and those harmed by wrong-doing. However, regulation can also have undesirable effects in terms of damaging competitive advantage, transnational cover arbitrage and various other inefficiencies in the insurance market.

Corporate governance

In order to guarantee the long term presence and operation of insurance companies in the economy, the players must continue to cultivate best practice forms of corporate governance. These practices must perform in the field of internal management and relationships with stakeholders, as well as the impact insurers bring to bear in a broader societal context.

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A full list of current IFoA research working parties can be found here.

The IFoA supports and sponsors research through member-led working parties in various practice areas and through commissioned external research such as that undertaken in universities. By supporting new research, we continue to further actuarial science and aim to provide members with cutting edge knowledge attuned to the realities of their working lives. This listing contains all IFoA research.

Research outputs can be accessed via Sessional Research Programme recordings and Research papers.

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The Insurance Industry Wants a World That Is Sustainable and Insurable

The insurance industry seeks to address challenges such as climate change and human rights via the Principles for Sustainable Insurance

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Leading Sustainable Organizations

A United Nations initiative, called Principles for Sustainable Insurance, is serving as a global framework for the insurance industry to address environmental, social, and governance challenges.

Insurance companies are uniquely positioned to address environmental, social, and governance (ESG) challenges such as climate change and human rights issues in their roles as risk managers, risk carriers, and investors. There are many reasons why environmental and human rights risks are relevant for the insurance industry; similarly, the importance of climate change-related liability risks has also become devastatingly clear to industry leaders. The insurance industry as a whole has taken note and begun to make strides in identifying and addressing these challenges.

Much of this work is being steered by the Principles for Sustainable Insurance (PSI) initiative, launched by the UN Environment Programme Finance Initiative (UNEP FI) in 2012. The Principles serve as a global framework for the insurance industry to address ESG risks and opportunities. The PSI are now backed by more than 80 insurance and stakeholder organizations worldwide, including insurers representing approximately 20% of world premiums, and $14 trillion (USD) in assets under management.

According to Butch Bacani, who leads the PSI at the UN, “the number and the momentum of insurance industry initiatives that promote sustainable development have been growing over the years. They span multiple issues, from increasing access to insurance and building disaster resilience, to mitigating and adapting to climate change. This year offers a strategic opportunity for convergence. New global and national policies on sustainable development, together with private-sector commitments and multi-stakeholder partnerships, can help harness the full potential of the insurance industry in promoting economic, social and environmental sustainability.”

Last March, the PSI launched the United for Disaster Resilience statement, a commitment to help implement the new UN global framework for disaster risk reduction. It emphasizes that the insurance industry is “well placed to understand the economic and social impact of disasters (…) especially in the context of climate change adaptation, and the need for climate change mitigation.” The PSI has raised its ambitions still further, and is now calling on insurers to make voluntary commitments that build disaster resilience and promote sustainable development.

At the Climate Finance Day in Paris on May 22, 2015, the CEO of global insurance and asset management company

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Olivier Jaeggi is founder and managing partner at ECOFACT. He can be reached at [email protected].

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New York’s Putting Some Green— $5 Million— Into Green Energy Insurance Innovations

New York State’s $5 million Insurance Innovation Prize is now open for applications.

Supported by the New York State Energy Research and Development Authority (NYSERDA), the new program offers awards for the “development and deployment of novel insurance solutions that promote and increase the adoption of energy transition technologies.” At least five winners will be chosen.

“This program was created to support increases in research and development activities that expand affordability and accessibility of energy-related technologies and narrow the gap between science, engineering, and underwriting approaches,” said Charlie Sidoti, executive director of Innsure, the innovation hub that is administering the prize competition.

Food for Thought for Insurance Innovators Investors Are Increasingly Interested in a Novel Type of Weather Insurance Viewpoint: Four Approaches to Expand Commercial P/C’s Market Relevance How the Insurance Universe Deals with Evolving Risks and Dispositions Marsh Launches First Dedicated Insurance for Hydrogen Projects AXIS Gets In-Principle Approval to Launch Energy Resilience Syndicate at Lloyd’s Evacuation Policies and Cat Strips: Wildfire Insurance and Reinsurance Ideas That Failed CEO Viewpoint: Predict and Prevent Just Makes Economic Sense Wind Turbines That Shake and Break Cost Their Maker Billions Electric Vehicles and the New Frontier They Represent for Auto Insurers Executive Viewpoint: Climate Protection Requires Tech Innovation Munich Re Specialty Offers ‘Green Solutions’ For Customers’ Net Zero Ambitions

The state wants to address the concern that many technologies required to achieve net-zero emissions by 2050 are still in the early phases of commercialization and are therefore difficult to insure.

The insurance market for clean energy assets alone is expected to reach $15 billion by 2030, according to the sponsors, who contend that there is also an opportunity for the creation of new energy transition products and policies in the home insurance and auto insurance sectors.

Prize applicants can be for profit or non-profit entities, but the solutions they propose must demonstrate a path toward financial self-sufficiency. Applicants may be located anywhere in the world; however, they will need to show they can bring an insurance product or policy to market in New York within 18 months of the grant start date.

As the program administrator, Cambridge, Mass.-based InnSure will work with underwriters and agents who can research, develop, and test the new insurance products and select the winners.

Once prize winners are announced, InnSure will support them in transitioning to in-market insurance solutions over a period of up to 18 months. Support services include activities such as raising risk capital and guidance for regulatory approvals in New York State; access to business, technology, and insurance consultants; and in-person summits that will serve as opportunities to network.

Non-binding letters of intent should be submitted by May 27. The application period will close on July 22. Full requirements for the application and details on award allocation can be found on the webpage .

The innovation program is one of several New York State initiatives created to tackle climate change.

This week Governor Kathy Hochul launched a voluntary climate action pilot program for hospitals. The program provides premium credits of up to $1 million to New York State Insurance Fund-insured hospitals that pledge to achieve net zero greenhouse gas emissions by 2050 and enhance their resilience to extreme weather events, helping mitigate the climate-related hazards that contribute to steep increases in workplace injuries and illnesses.

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