Sociology of Insurance

Millions of motorists file damage claims with insurance companies each day around the
globe. A minor accident, storm damage, or an actual collision, every claim brings up a system that
has gone through tremendous changes over the last decade. Human experts used to go on-site to
examine damages in person using a checklist, while now the same thing happens almost instantly
as AI creates valuation numbers for insurance companies. Insurance firms are increasingly relying
on artificial intelligence technology that can speed up the process, reduce risks of fraud, and ensure
consistent results. At first glance, it might appear like an inevitable trend because AI doesn’t have
biases, cannot get tired, and can process large amounts of data in a very short period of time. Yet
the truth is that AI-generated valuations are often unfair, unexplainable, and opaque even to their
creators.

The challenges that the insurance industry faced have transformed the sector through the insurtech in convenient path and becoming rudimentary function in technological respond to needs of customers and businesses, the interconnectivity among insurers and policyholders and the new possibility access in terms of hedging risks. The aim of the research is to announce and herald the process how technologies are affecting and evolving the insurance industry and the facing challenges for the companies. The study presented how datasets have transformed the insurance business and markets, also the process of simplifying claims management. Also, the paper shows how the artificial intelligence has evolved the traditional insurance from the starting point until the ending contract process, the usage of artificial intelligence as interacting tool between companies and clients or policyholders. The last chapter of the research includes quantitative interpretation of data such insurtech investments, the highest insurtech deals and USA as the leading insurance market. The innovation process including new effective technologies is considered important driver for the changes in all sectors, especially in financial sector resulting with immeasurable benefits and efficiency gains, although technological changes were accepted with doubt and uncertainty.

Organisations and insurance companies are not utilising business data and analytical methods to gain full advantage of accurate representation of risk, which can lead to further risk such as underinsurance, overpriced policies, and inaccurate claims assessment. Many companies organise their own insurance and risk management internally and rely on external experts such as insurance companies, brokers, and underwriters to help determine requirements. As part of this set up, there is a reliance on data derived from the organisation to inform policy placement, pricing of insurance policies and to accurately represent risk. The nature for enhancing the operational efficiency of insurance products using Big Data Analytics (BDA) is found to be a common concept in the insurance industry. The research work relating to insurance risks and cyber security is still in its infancy. Therefore, the research aim is to investigate how best to derive business insight from the insurance and risk division of an organisation using Big Data Analytics (BDA). The research considered security metrics for risk, control and mitigation based on the organisation's reaction to cyber risk for data accuracy. This paper reviews research previously conducted in the area with the aim of understanding which Big Data Analytics (BDA) methods are currently used and in which areas of the insurance industry. The findings show that Big Data Analytics (BDA) methods such as predictive analytics, artificial intelligence, machine learning and random forest regression are utilised in areas of insurance such as underwriting, claims and pricing.

Usage-based insurance (UBI) is based on the idea that customers who are safer drivers should pay less for a policy than those who are less prudent. It is widely assumed that there is an incentive for insurers to use this technology, as it could make riskier drivers more likely to use non-telematics-based insurers. However, this hypothesis has not been verified empirically. In order to fill this research gap, a study (between December 2021 and March 2022) assessing the relationship between driving style and willingness to purchase a UBI-based policy and the relationship between risk propensity and willingness to purchase a UBIbased policy was conducted. The study, in the form of a questionnaire, was conducted in Poland and Spain. The samples consisted of 181 respondents in Poland and 51 in Spain. It was found that individual driving style was correlated with willingness to accept a UBI-based policy that included discounts and surcharges. It was also shown that propensity to risk influences willingness to purchase a UBI-based policy that offers discounts only to some extent. The study used suitable statistical measures and tests such as a chi-square test, U Mann-Whitney U test and a Kruskal-Wallis test. Results are significant for insurance companies that are going to introduce UBI. They allow shaping the product so that potential customers are interested in buying it, which, as research shows, may bring benefits to both insured and insurance companies.

The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

The challenges that the insurance industry faced have transformed the sector through the insurtech in convenient path and becoming rudimentary function in technological respond to needs of customers and businesses, the interconnectivity among insurers and policyholders and the new possibility access in terms of hedging risks. The aim of the research is to announce and herald the process how technologies are affecting and evolving the insurance industry and the facing challenges for the companies. The study presented how datasets have transformed the insurance business and markets, also the process of simplifying claims management. Also, the paper shows how the artificial intelligence has evolved the traditional insurance from the starting point until the ending contract process, the usage of artificial intelligence as interacting tool between companies and clients or policyholders. The last chapter of the research includes quantitative interpretation of data such insurtech investments, the highest insurtech deals and USA as the leading insurance market. The innovation process including new effective technologies is considered important driver for the changes in all sectors, especially in financial sector resulting with immeasurable benefits and efficiency gains, although technological changes were accepted with doubt and uncertainty.

Usage-based insurance (UBI) is based on the idea that customers who are safer drivers should pay less for a policy than those who are less prudent. It is widely assumed that there is an incentive for insurers to use this technology, as it could make riskier drivers more likely to use non-telematics-based insurers. However, this hypothesis has not been verified empirically. In order to fill this research gap, a study (between December 2021 and March 2022) assessing the relationship between driving style and willingness to purchase a UBI-based policy and the relationship between risk propensity and willingness to purchase a UBI-based policy was conducted. The study, in the form of a questionnaire, was conducted in Poland and Spain. The samples consisted of 181 respondents in Poland and 51 in Spain. It was found that individual driving style was correlated with willingness to accept a UBI-based policy that included discounts and surcharges. It was also shown that propensity to risk inf...

Intelligent Transportation Systems (ITS) cover a variety of services related to topics such as traffic control and safe driving, among others. In the context of car insurance, a recent application for ITS is known as Usage-Based Insurance (UBI). UBI refers to car insurance policies that enable insurance companies to collect individual driving data using a telematics device. Collected data is analysed and used to offer individual discounts based on driving behaviour and to provide feedback on driving performance. Although there are plenty of advertising materials about the benefits of UBI, the user acceptance and the usability of UBI systems have not received research attention so far. To this end, we conduct two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries from a web forum of a German insurance company. We find that under certain circumstances, UBI provokes dangerous driving behaviour. These situations could be mitigated by making UBI transparent and the feedback customisable by drivers. Moreover, the country driving conditions, the policy conditions, and the perceived driving style influence UBI acceptance.

Intelligent Transportation Systems (ITS) cover a variety of services related to topics such as traffic control and safe driving, among others. In the context of car insurance, a recent application for ITS is known as Usage-Based Insurance (UBI). UBI refers to car insurance policies that enable insurance companies to collect individual driving data using a telematics device. Collected data is analysed and used to offer individual discounts based on driving behaviour and to provide feedback on driving performance. Although there are plenty of advertising materials about the benefits of UBI, the user acceptance and the usability of UBI systems have not received research attention so far. To this end, we conduct two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries from a web forum of a German insurance company. We find that under certain circumstances, UBI provokes dangerous driving behaviour. These situations could be mitigated by making UBI transparent and the feedback customisable by drivers. Moreover, the country driving conditions, the policy conditions, and the perceived driving style influence UBI acceptance.

Intelligent Transportation Systems (ITS) cover a variety of services related to topics such as traffic control and safe driving, among others. In the context of car insurance, a recent application for ITS is known as Usage-Based Insurance (UBI). UBI refers to car insurance policies that enable insurance companies to collect individual driving data using a telematics device. Collected data is analysed and used to offer individual discounts based on driving behaviour and to provide feedback on driving performance. Although there are plenty of advertising materials about the benefits of UBI, the user acceptance and the usability of UBI systems have not received research attention so far. To this end, we conduct two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries from a web forum of a German insurance company. We find that under certain circumstances, UBI provokes dangerous driving behaviour. These situations could be mitigated by making UBI transparent and the feedback customisable by drivers. Moreover, the country driving conditions, the policy conditions, and the perceived driving style influence UBI acceptance.

This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this ar...

This article draws from two conceptual lenses – the sociology of expectations and market studies – to investigate the relationship between technology hype and market investments: which promises and expectations surround hype and how they come together to shape actors’ investments in an emerging market. We address this question through analysing a contemporary hype in a technology marketing context: digital healthcare. Our aim is to trace how market actors create, support and evaluate a market hype; how hype and market investments are related; and whether hype contributes to irreversibilities in shaping emerging market forms and categories. Our study indicates that hypes contribute tangibly to producing the market, not least by channelling financial, symbolic and material market investments. Furthermore, by highlighting how socio-economic, technological and policy promises become affectively loaded through circulation, we add a novel dimension to existing insights into the socio-cogn...

The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

Insurance 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 setup , 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 adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

Insurtech is the latest buzz word that is shaking up the insurance world globally. In the simplest terms, insurtech can be defined as insurance coupled with technology. Though insurtech is still believed to be in nascent stages in India, the insurtech start-ups in India are changing the market dynamics to a great extent. The present study aims to analyse the financial soundness of insurtech companies in India and also to compare the financial soundness of these two companies. Two leading insurtech companies namely, Go digit and Acko are taken as sample of the study. The financial data pertaining to these two companies for a period of three years from 2017-18 to 2019-20 was used for the analysis. The financial indicators of CARAMELS model were used for analysing the financial soundness of these companies. Six parameters of the CARAMELS model namely C - Capital adequacy, A - Asset quality, RA - Reinsurance and Actuarial issues, M - Management soundness, E - Earnings and profitability ...