Analyzing fraud rate based on insurance contract reports using meta- deep stacking approach

Abstract

Insurance fraud, particularly in automobiles, presents substantial challenges for insurance companies worldwide. Fraudulent activities by policyholders, such as document falsification and evidence fabrication, aim to deceive insurers and unlawfully obtain funds, resulting in significant financial losses. However, many insurers rely solely on traditional financial assessment methods, which may be insufficient in detecting fraud, especially in cases involving sophisticated schemes or high claim volumes. This study seeks to develop an effective fraud detection model tailored to the characteristics of real-world data using machine learning algorithms. The dataset comprises 1000 insurance claims related to car collisions across seven US states in 2015. Results indicate that employing an Early Fusion deep learning model, integrating Decision Tree and Random Forest, mitigates the limitations of traditional models. The findings demonstrate that the proposed model outperforms previous traditional models and enhances fraud detection capabilities. Building on these findings, the objective is to improve fraud detection within the Vietnamese insurance industry, thereby reducing instances of contract fraud. Our framework supports sustainable development by promoting innovation, efficiency, and technological advancement for SDG 9. It also combats fraud, strengthens institutions, and fosters transparency and accountability for SDG 16 in the insurance sector. Future research will focus on leveraging prominent deep learning models globally and utilizing diverse datasets to refine and develop the most robust feasible model.