AI-Powered Fraud Detection in Financial Transactions Using Deep Learning

Prof. Linkon Canebra

Abstract


Financial fraud detection is critical for maintaining the integrity of financial systems. This paper introduces an AI-powered fraud detection system that utilizes deep learning models to analyze financial transactions in real-time. By processing transaction data, including user behavior, transaction amount, and historical patterns, the system identifies fraudulent activities with high accuracy. The proposed model outperforms traditional rule-based systems, achieving lower false-positive rates and better adaptability to evolving fraud tactics. The findings underscore the potential of deep learning in enhancing financial security and preventing fraud.

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