An Intelligent Machine Learning Framework for Credit Card Fraud Detection
Keywords:
Credit Card Fraud, Fraud Detection, Financial Transactions, Machine Learning, Card-Not-Present Fraud, Identity Theft, Skimming AttacksAbstract
Credit card fraud refers to the unauthorized use of an individual’s credit card or the theft of card-related information to obtain financial benefits. Fraudulent activities take multiple forms, including counterfeit card fraud, card-not-present fraud, identity theft, and skimming attacks. With the rapid growth of online transactions and digital payment systems, credit card fraud has become a major concern for banks, financial institutions, and consumers worldwide. Traditional fraud detection techniques are often inadequate in handling the complexity, scale, and evolving nature of modern fraud patterns. In recent years, there has been significant interest in applying machine learning techniques to credit card fraud detection due to their ability to analyze large volumes of transaction data and identify hidden patterns associated with fraudulent behavior. Credit card fraud results in substantial financial losses for individuals and organizations, increases operational costs for banks, and undermines trust in the financial system. Victims may suffer not only monetary losses but also emotional distress and long-term damage to their credit records. Therefore, effective and intelligent fraud detection systems are essential to minimize unauthorized transactions, protect customer assets, and ensure the stability and reliability of financial services.
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