Editorial: The personalisation of insurance: Data, behaviour and innovation (original) (raw)
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Personalization as a promise: Can Big Data change the practice of insurance?
Big Data & Society
The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An ex...
'Happy failures': Experimentation with behaviourbased personalisation in car insurance
Big Data & Society, 2020
nsurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies (STS), this article investigates the role of economic experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin’s distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence (on the link between driving style behaviour and accident losses), could be considered a ‘happy’ event. We conclude by highlighting the role of economic experiments ‘in the wild’ for the making of future markets of data-driven personalisation.
The Effect of Big Data on the Development of the Insurance Industry
Business ethics and leadership, 2023
Big data is at the heart of the insurance industry through the uses it provides, where the year 2022 is considered the beginning of the "digital revolution" when humans were able to store more digital information in technological tools than ever before. Research results have shown the impact relationship between big data and various industries, including the insurance industry. Big data has improved all aspects of the insurance process, from pricing and underwriting to claims management and customer service to ultimately more effective risk management. Based on practical and theoretical practices in this framework, the question arises whether big data has brought about development in the insurance industry. Therefore, the purpose of this study was to gain a better understanding of the impact of big data on all aspects of the insurance industry. The research findings showed that the quantity and quality of data collected and used by insurance companies directly impact the services produced and developed. Big data enables insurers to identify patterns, trends and behaviors, allowing them to develop customized products and services. Also, by collecting and utilizing quality big data, insurance companies can provide more efficient and effective services, improving customer satisfaction and increasing profitability. Although big data is a lucrative opportunity for the insurance industry, it is also a threat as companies that need the means to access big data, technologies and skills will see their competitiveness drop significantly in the future. On the other hand, intermediary platforms, particularly GAFTA (Google, Apple, Facebook, Twitter, Amazon) that control the entire data value chain, can seek a large percentage of profits by providing the value chain to insurers, or the purchase of these platforms for vulnerable insurance companies, allowing them to dominate the insurance market.
From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance
2020
The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem-shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour.
Using Risk Analytics to Prevent Accidents Before They Occur – The Future of Insurance
2021
While insurance was originally devised as a safety net that steps in to compensate for financial losses after an accident has occurred, the information generated by sensors and digital devices now offers insurance companies the opportunity to transform their business by considering prevention. We discuss a new form of risk analytics based on big data and algorithmic prediction in the insurance sector to determine whether accidents could indeed be prevented before they occur, as some now claim is possible. We will use the example of motor insurance where risk analytics is more advanced. Finally, we draw conclusions about insurance’s new preventive role and the effect it may have on the policyholders’ behavior.
From Actuarial to Behavioural Valuation. The Impact of Telematics on Motor Insurance
2022
Algorithmic predictions are used in insurance to assess the risk exposure of potential customers. This article examines the impact of digital tools on the field of motor insurance, where telematics devices produce data about policyholders' driving styles. The individual's resulting behavioural score is combined with their actuarial score to determine the price of the policy or additional incentives. Current experimentation is moving in the direction of proactivity: instead of waiting for a claim to arise, insurance companies engage in coaching and other interventions to mitigate risk. The article explores the potential consequences of these practices on the social function of insurance, which makes risks bearable by socialising them over a pool of insured individuals. The introduction of behavioural variables and the corresponding idea of fairness could instead isolate individuals in their exposure to risk and affect their attitude towards future initiatives.
24th International Conference on Business Information Systems (BIS 2021), 2021
AI and data technologies are a catalyst for fundamental changes to insurance business models. The current upheaval is seeing some incumbent insurers trying to do the same more effectively, while others evolve to fully utilize the new capabilities and users these new technologies bring. At the same time, technologically advanced organizations from outside the sector are entering and disrupting it. Within this upheaval however, there are signs of a convergence towards an ideal and prevailing business model. This research identifies one exemplar incumbent and one disruptor and evaluates whether their models are converging and will become similar eventually. The findings support a high degree of convergence, but some differences are likely to remain even after this transitionary period. The differences identified are firstly in the evaluation of risk and secondly that traditional insurers prioritize revenue generation from what is their primary activity, while new entrants prioritize expanding their user base.
Privacy: A Growing Risk in the Insurance Industry
2019
With the rapid development of technology-based products and services in recent years, information technology seeps into the pore of people's daily life by adding value-added services in products. Insurance has seen a rapid increase in technological advancements, which has led to an evergrowing risk of privacy, not just in this industry but across all the technology sector. Given this scenario, more data is collected, which increases the risk of data theft, and cyber risk and existing research shows that most organizations do not have enough resources to prevent data breaches, deal with notification responsibilities, and comply with privacy laws. Talesh, S. A. (2018) shows how insurance companies play a critical yet unrecognized role in assisting organizations in complying with privacy laws and dealing with cyber theft. The scope of this research is to make insurance companies be on the front foot of the existing and upcoming privacy laws being incorporated and recommending efficient ways to comply with these laws internally and when exposed to an audit. Additionally, another goal of this paper is to identify and suggest ways to increase user adoption within the Telematics segment, which has been a talking point in the automobile segment of the insurance industry where it has been adopted by 20% of the consumer market as of 2016 but has a potential of taking over the entire section as more data is collected as the days pass by. With such changes, the terms and conditions of every privacy policy are becoming something more than just a fine print.
Proceedings of the 17th International Conference on e-Business, 2020
The proliferation of the big data movement has led to volumes of data. The data explosion has surpassed enterprises’ ability to consume the various data types that may exist. This paper discusses the opportunities and challenges associated with implementing data lakes, a potential strategy for leveraging data as a strategic asset for enterprise decision-making. The paper analyzes an information ecosystem of an Insurance Company environment. There are two types of data sources, information systems based on a transactional databases for recording claims, as the basis of financial administration and systems policies. There exists neither Data Warehouse solutions nor any other data collection solutions dedicated to utilizing by Data Science methods and tools. The emerging technologies provide opportunities for synergy between the traditional Data Warehouse and the most recent Data Lake approaches. Therefore, it seems feasible and reasonable to integrate these two architecture approaches to support data analytics on several aspects of insurance, financial activities, risk analysis, prediction and forecasting.