Abstract

In the swiftly advancing digital financial arena, fraud detection and prevention have emerged as crucial issues for both institutions and customers. Traditional fraud detection frameworks, dependent on rigid rules and human supervision, frequently struggle to adapt to the ever intricate strategies utilized by fraudsters. This article examines the benefits of machine learning (ML) methodologies to improve fraud detection systems via the study of fingerprint biometrics. ML models, like Random Forest, XGBoost, CatBoost, and Deep Neural Networks (DNN), were utilized to identify fraudulent transactions by examining biometric data and financial transaction patterns. The study identifies CatBoost as the most effective model, exhibiting higher performance in critical measures including precision, recall, and F1 score. The incorporation of fingerprint biometrics enables these AI-driven models to adopt a proactive strategy for fraud detection, recognizing fraudulent activity in real time and provide a strong defense against advancing fraud techniques. The essay addresses the significance of data preprocessing, feature engineering, and ongoing model learning in developing a scalable and effective fraud detection system. The findings highlight the capacity of AI-driven technologies to transform fraud protection in financial services, providing enhanced precision and flexibility against evolving fraudulent methods.

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