Insurance Fraud Detection and Analysis for Insurance Claim

Abstract

As global economies continue to expand, safeguard ing financial systems against fraudulent activities has become a critical priority. Among these, insurance fraud stands out as a major concern because it causes billions of dollars in losses for both companies and policyholders. With the increasing volume and complexity of claims, traditional methods of fraud detection are no longer sufficient. Advanced techniques such as data-analytics and machine-learning provide powerful-tools for addressing this challenge by automating the detection process and improving accuracy. To design an effective solution, it is first essential to study existing research and identify the most effective approaches that have been applied in this domain. Building on these insights, we propose a machine learning–based model capable of identifying suspicious insurance claims. Such a system not only assists companies in reducing financial losses and operational overhead but also enables quicker responses to potential fraud, thereby improving overall efficiency and trust in the insurance sector.