Predicting Fraud in Auto Insurance Claims

Fraudulent activities in the domain of auto insurance claims pose significant financial risks to insurance companies, leading to substantial losses and compromised customer trust. This research explores the application of machine learning models, including XGBoost, Logistic Regression, Multi-Layer Perceptron (MLP), and Decision Tree classifiers, to predict fraudulent insurance claims. The study leverages key policyholder attributes, including demographic details, policy specifics, and socio-economic factors, to build robust fraud detection models. The experimental results provide valuable insights into the effectiveness of different machine learning techniques in mitigating fraud risks. Additionally, this paper discusses challenges, regulatory considerations, and real-world applications of AI in fraud prevention.