AI and Fraud Detection in Non-Life Insurance: Techniques and Efficacy

Abstract

The use of Artificial Intelligence (AI) in the non-life insurance industry has transformed the process of detecting fraud by providing sophisticated methods to identify fraudulent activity with enhanced accuracy and effectiveness. This research dissertation investigates the use of artificial intelligence (AI)-based technologies, including machine learning (ML), natural language processing (NLP), and predictive analytics, to identify and prevent fraudulent activities in non-life insurance sectors such as automobile, property, and health insurance. Through the examination of past data, artificial intelligence models have the capability to detect trends, irregularities, and warning signs that suggest fraudulent activities. This process allows insurers to reduce financial losses and improve operational effectiveness. This article assesses the effectiveness of various artificial intelligence (AI) approaches by comparing them to conventional ways of detecting fraud. It also addresses issues such as data privacy, algorithmic bias, and the need for regulatory frameworks. Evidence suggests that artificial intelligence greatly enhances the precision and speed of fraud detection, while decreasing the occurrence of false positives, therefore making a valuable contribution to a more safe and economically efficient insurance industry. Furthermore, our work suggests new avenues for AI advancement in fraud detection, underscoring the need of ongoing investment in AI infrastructure and cooperation across industries.