EIOPA published a report on the use of big data analytics, along with its associated benefits and risks, throughout the insurance value chain, with focus on motor and health insurance business lines. This report is based on a thematic review that was launched in the summer of 2018, with about 222 insurance undertakings and intermediaries from 28 jurisdictions participating in the review.
The thematic review has revealed a strong trend toward increasingly data-driven business models throughout the insurance value chain in motor and health insurance:
- The use of data outsourced from third-party data vendors and their corresponding algorithms used to calculate metrics, such as credit scores, driving scores, and claims scores, is relatively extended and this information can be used in technical models.
- Big data analytics tools such as artificial intelligence or machine learning are already actively used by 31% of firms and another 24% are at a proof of concept stage. Models based on these tools are often correlational—not causative—and they are primarily used on pricing and underwriting and claims management.
- Cloud computing services, which reportedly represent a key enabler of agility and data analytics, are already used by 33% of insurance firms, with a further 32% saying they will be moving to the cloud over the next three years. Data security and consumer protection are key concerns of this outsourcing activity.
- Big data analytics enables the development of new rating factors, leading to smaller risk pools and a larger number of them. Most rating factors have a causal link while others are perceived as being a proxy for other risk factors or wealth/price elasticity of demand.
- However, as yet, no evidence exists that an increasing granularity of risk assessments is causing exclusion issues for high-risk consumers, although firms expect the impact of big data analytics to increase in the years to come.
EIOPA concludes from the review that many opportunities are arising from big data analytics, both for the insurance industry and for consumers. Although insurance firms have in place or are developing sound data governance arrangements, there are additional risks arising from big data analytics that need to be further addressed in practice. Some of these risks are not new, but their significance is amplified in the context of big data analytics. This is particularly the case regarding ethical issues with the fairness of the use of big data analytics and regarding the accuracy, transparency, auditability, and explainability of certain big data analytics tools such as artificial intelligence and machine learning. EIOPA will further assess how artificial intelligence and machine learning can be best supervised in practice. In this context, different options will be considered, such as introducing specific governance requirements for specific big data analytics tools and algorithms. This could include reviewing the role that Solvency II’s key functions (and in particular the actuarial function) should play in this context.
InsurTech Task Force of EIOPA will, in 2019, conduct further work in the areas of artificial intelligence and machine learning in collaboration with the industry, academia, consumer associations, and other relevant stakeholders. The work being done on artificial intelligence by the Joint Committee of the ESAs as well as in other international fora will also be taken into account. In addition, EIOPA will explore third-party data vendor issues, including transparency in the use of rating factors in the context of the EU-U.S. insurance dialog. Furthermore, EIOPA will develop guidelines on the use of cloud computing by insurance firms and will start a new workstream assessing new business models and ecosystems arising from insurtech. EIOPA will also continue its ongoing work in the area of cyber insurance and cyber security risks.
Keywords: Europe, EU, Insurance, Big Data, Fintech, Thematic Review, Insurtech, Artificial Intelligence, Machine Learning, EIOPA
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