Authors: Sakthivel S, Vikash P
Abstract: The rapid expansion of digital financial services has significantly transformed the global financial ecosystem by enabling fast, convenient, and seamless transactions. However, this transformation has also increased the vulnerability of financial systems to fraudulent activities such as credit card fraud, identity theft, phishing attacks, insider fraud, and money laundering. Financial fraud results in substantial economic losses, damages institutional reputation, and undermines customer trust in digital banking systems. Traditional fraud detection mechanisms primarily rely on rule-based systems and manual audits, which are reactive, inflexible, and often incapable of detecting complex and evolving fraud patterns in real time. Advancements in artificial intelligence (AI), machine learning (ML), and data analytics have paved the way for intelligent financial fraud detection systems capable of processing large volumes of transaction data efficiently. By learning patterns from historical transaction data and identifying anomalies, AI-driven systems enable early detection and prevention of fraudulent activities. This paper presents an AI-based financial fraud detection framework that integrates data preprocessing, feature engineering, and machine learning-based classification for real-time fraud analysis. The proposed system aims to improve detection accuracy, reduce false positives, and enhance the overall security of digital financial transactions. Experimental results and analysis demonstrate that intelligent fraud detection systems provide scalable, adaptive, and reliable solutions for modern financial environments.
