Improving Insurance Fraud Detection with Generated Data

Insurance fraud poses a significant threat to the industry, with estimated annual losses of $40 billion. Traditional efforts to combat fraud have relied on identifying fraudulent traits, but machine learning (ML) techniques offer promising avenues for detection. However, ML methods face challenges due to data scarcity and class imbalance in fraud datasets, hindering model efficacy. Recent advancements in Generative Adversarial Networks (GANs), present a solution by generating synthetic data resembling real instances. Accurate representational data generation also offers an avenue of preserving data privacy. In this paper, we propose a novel approach leveraging GANs to generate accurate representational data of fraudulent transactions, addressing data scarcity and privacy concerns. By incorporating generated data into model training, we show predictive performance improvement by up to 24%. Our empirical results demonstrate the efficacy of the proposed method in augmenting fraud detection capabilities while preserving privacy, thus offering a promising direction for improving insurance fraud detection. Our research contributes to advancing fraud detection in the insurance domain, with potential applications in other sectors facing similar challenges. Future research directions include exploring other generative models and combining them with traditional ML techniques to further enhance fraud detection capabilities.