Forensic Audit in Fraud Detection and Prevention for General Insurance Claims: A Literature Review

Abstract

This paper presents a comprehensive literature review on the application of forensic audit techniques to detect and prevent fraud in general insurance claims. The review encompasses studies published between 2020 and 2025, focusing on the evolving role of forensic auditing within the insurance industry. Our analysis reveals several key themes: 1) The increasing sophistication of insurance fraud schemes has necessitated more advanced forensic audit methods, including the integration of data analytics and artificial intelligence. 2) Forensic auditing plays a critical role in uncovering existing fraud, developing preventive measures, and strengthening internal control systems within insurance companies. 3) There is a growing trend toward using predictive modeling and machine learning algorithms to identify potential fraudulent claims before they are processed. 4) The effectiveness of forensic auditing is significantly enhanced when combined with other fraud detection techniques, such as data mining and network analysis. 5) The regulatory framework and industry standards for forensic auditing in insurance are still evolving, with variations across jurisdictions. This review also highlights several research gaps, including the need for further empirical studies on the cost-effectiveness of forensic audit programs in insurance companies, the impact of emerging technologies such as blockchain on forensic audit practices, and ethical considerations in the use of advanced data analytics for fraud detection. This study contributes to the existing literature by providing a synthesized view of current forensic audit practices in general insurance fraud detection and prevention while identifying future research directions to address the evolving challenges in this field