A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts

TL;DR

This paper presents an unsupervised machine learning framework for detecting anomalies in life insurance contracts, comparing various methods and proposing automation to aid adoption by companies with limited data science expertise.

Contribution

It introduces a comparative analysis of classical and modern unsupervised anomaly detection methods tailored for life insurance data, and explores automation strategies for practical deployment.

Findings

Modern methods outperform classical ones in anomaly detection accuracy.

Automation techniques improve accessibility for non-data scientists.

Performance varies across different datasets and anomaly types.

Abstract

Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or inter\-actions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their performance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists.