DEVELOPING AI-BASED FRAUD DETECTION SYSTEMS FOR BANKING AND FINANCE

Abstract

Financial fraud poses a significant threat to the security and integrity of banking and digital payment systems, leading to substantial economic losses and undermining customer trust. Traditional fraud detection techniques, including rule-based algorithms and human evaluations, have challenges in scalability and adaptability to changing fraudulent strategies. This study introduces a sophisticated AI-driven fraud detection system that use machine learning methodologies to identify fraudulent transactions in real-time. The suggested method incorporates data preprocessing, feature engineering, model training, and deployment pipelines. A variety of techniques, such as logistic regression, decision trees, random forests, and neural networks, are applied and assessed on a real-world financial transactions dataset. The system achieves high precision, recall, and AUC scores, demonstrating its ability to minimize false positives while maintaining high detection rates. The end-to-end implementation also includes containerized deployment and monitoring using modern MLOps tools. This work provides a scalable, adaptive, and accurate approach for financial institutions to combat fraud more effectively and securely.