Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Real-Time Credit Card Fraud Detection Using Machine Learning

Author(s) Mr. Mannava Srinath, Mr. Tirumala Karthik, Mr. Mujthaba Gulam Muqeeth
Country India
Abstract Credit card-based financial transactions form the backbone of today's digital economy, giving millions of consumers unparalleled convenience in purchases, online payments, and fund transfers. The widespread adoption of credit card-based transactions has concurrently opened avenues to potential fraud-related losses leading to significant losses among individuals, businesses, and financial organizations. Traditional fraud detection systems, which are rule based, give preliminary protection but are not ready to cope with complex and variable fraud
patterns that adapt over time. This calls for intelligent fraud detection techniques that apply machine learning to analyze the behaviours of transactions and single out fraudulent activities from the genuine ones. It presents a credit card fraud detection system developed using a Decision Tree classifier, considering interpretability, operational efficiency, and modeling of non-linear decision
boundaries. This model examines features in anonymized transactions in order to find unusual patterns that set them apart from typical customer profiles. This roadmap contains extensive data preprocessing, the removal of duplicates, normalization, and class imbalance. Experimental evaluation showed that the Decision Tree classifier yielded reliable performance on fraud transaction detection while maintaining a low false positive rate. This proves that
interpretable machine learning models can be embedded into real-world financial systems to enhance security, transparency, and trust in digital transactions.
Keywords Credit Card Fraud Detection, Machine Learning, Decision Tree, Anomaly Detection, Financial Security, Data Preprocessing
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-27
DOI https://doi.org/10.63363/aijfr.2025.v06i06.2646
Short DOI https://doi.org/hbg7bw
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