AI, insurance, discrimination and unfair differentiation. An overview and research agenda

TL;DR

This paper reviews how AI-driven data analysis and real-time consumer monitoring in insurance could lead to discrimination and unfair treatment, highlighting societal implications and future research directions.

Contribution

It provides an overview of AI applications in insurance and explores potential discriminatory effects, proposing a research agenda to address fairness concerns.

Findings

AI enables more precise risk assessment and real-time monitoring.

Potential for discriminatory effects and unfair differentiation.

Highlights need for fairness-aware AI in insurance.

Abstract

Insurers underwrite risks: they calculate risks and decide on the insurance price. Insurers seem captivated by two trends enabled by Artificial Intelligence (AI). First, insurers could use AI for analysing more and new types of data to assess risks more precisely: data-intensive underwriting. Second, insurers could use AI to monitor the behaviour of individual consumers in real-time: behaviour-based insurance. For example, some car insurers offer a discount if the consumer agrees to being tracked by the insurer and drives safely. While the two trends bring many advantages, they may also have discriminatory effects on society. This paper focuses on the following question. Which effects related to discrimination and unfair differentiation may occur if insurers use data-intensive underwriting and behaviour-based insurance?