AI-Driven Personalized Risk Management in the Insurance Sector

Abstract

Introduction: In the fiercely competitive insurance landscape, embracing personalized risk management offers insurers a strategic advantage, enabling the provision of innovative, customer-centric solutions tailored to individual policyholders.

Purpose: The purpose of the study was to identify AI factors that supports risk management in the insurance sector to assist investors and users to manage their risk using different AI tools.

Methodology: In the current study, primary data are collected by using simple random sampling from 372 respondents. A questionnaire was sent to more than 500 respondents, but the final sample size was 372 based on their complete responses and data provided in the questionnaire. Target audience was insurance services/policy users. The study is empirical in nature, and data analysis was done using factor analysis.

Findings: It was found that there are three major factors which can affect risk management in insurance such as better risk management, acceptance as well as anticipation, and better customization can be possible for the customers by utilizing AI-driven technology in insurance. The early identification of fraudulent behavior can enable the insurers in loss reduction as well as promoting competitive price policy for honest customers. An automative mechanism can be helpful in adjusting premiums on the basis of real-time assessment of risk factors.

Citation

Agrawal, A., Kumar, T., Agarwal, R. and Gupta, A. (2024), "AI-Driven Personalized Risk Management in the Insurance Sector", Taneja, S., Kumar, P., Reepu, Kukreti, M. and Özen, E. (Ed.) Data Alchemy in the Insurance Industry, Emerald Publishing Limited, Leeds, pp. 27-39. https://doi.org/10.1108/978-1-83608-582-920241006