Using GANs for Insurance Claims Prediction: Improving Claims Automation

Processing insurance claims is a complex task that requires accurate predictions to speed up approvals, reduce fraud, and improve customer satisfaction. Traditional models often struggle with complex and unbalanced data. This paper explores how Generative Adversarial Networks (GANs) can help make insurance claims predictions more accurate by creating synthetic claim scenarios. GANs can generate realistic claim cases to improve training, leading to faster and more precise decisions. We propose a framework for using GANs in claims processing, compare its performance with traditional machine learning models, and discuss ethical and regulatory challenges. Experiments show that GAN-enhanced models perform better than traditional methods in accuracy and reliability. This study highlights how GANs can optimize claims automation and reduce fraud risks in the insurance industry.