CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS

Abstract

Credit card fraud has become a major challenge for financial institutions and online payment systems due to the rapid growth of digital transactions and e-commerce platforms. Fraudulent activities can lead to significant financial losses, compromised customer trust, and operational risks for banking organizations. Traditional fraud detection systems often rely on predefined rules and manual monitoring, which may be ineffective in identifying sophisticated and evolving fraud patterns. This paper presents a credit card fraud detection framework using machine learning algorithms to accurately identify fraudulent transactions in real time. The proposed system utilizes transaction data, customer behavior patterns, spending habits, and transaction attributes to train machine learning models capable of distinguishing between legitimate and fraudulent activities. Various classification algorithms are employed to analyze transaction characteristics and detect anomalies with high precision. Data preprocessing, feature engineering, and model optimization techniques are incorporated to improve detection accuracy and reduce false positive rates. Experimental analysis demonstrates that machine learning-based fraud detection significantly enhances transaction security, improves fraud identification efficiency, and supports proactive risk management. The proposed framework provides an intelligent and scalable solution for safeguarding digital payment systems against financial fraud.