Advancing Car Insurance Fraud Detection: An Automated Deep-Learning Approach

Car insurance fraud poses a significant challenge in the insurance industry, leading to substantial financial losses and operational inefficiencies. Current methods of managing fraud rely heavily on manual investigation processes and rule-based systems. However, these approaches have inherent limitations, including delays in identifying fraudulent claims, human error, and an inability to keep pace with evolving fraudulent tactics. The need for a more robust and efficient system to combat car insurance fraud is evident. An automated system offers the potential to streamline fraud detection processes, reducing reliance on human intervention and significantly improving operational efficiency. Rapid and accurate identification of potential fraud cases not only expedites claims processing but also allows for a more proactive response to emerging fraud schemes. The solution proposed in this work introduces an end-to-end pipeline leveraging the capabilities of state-of-the-art Deep Learning models. The pipeline involves key steps, including data preprocessing for comprehensive feature extraction, anomaly detection models for flagging potentially fraudulent claims, advanced image processing, and deep metric learning techniques to understand the structural similarities between damages. By addressing the limitations of current methods, the proposed approach aims to enhance the overall effectiveness of fraud detection, contributing to a more streamlined insurance claims process and a proactive stance against evolving fraudulent activities.