Discovering dynamic adverse behavior of policyholders in the life insurance industry

Abstract

Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders’ behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This study aims to analyze the life insurance policyholder’s behavior to identify adverse behavior (AB). In this study, we present a novel association rule learning-based approach ‘ARLAS’ to detect the AS behavior of policyholders. In addition to the original data, we further created a synthetic AS dataset by randomly flipping the attribute values of 10% of the records in the test set. The experiment results on 31,800 Australian life insurance users show that the proposed approach achieves significant gains in performance comparatively.