Authors : Dr. Pushpa Ravikumar; Gowrav A. S.; Dr. Arpitha C. N.; Dr. Anser Pasha C. A.

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/v6xbycun

Scribd : https://tinyurl.com/3svsxwn7

DOI : https://doi.org/10.38124/ijisrt/26jun1943

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid growth of digital banking, online transactions, and the electronic payment of the systems has been significantly increased the use of credit cards, results in the parallel rise in fraudulent activities. Traditional methods of detecting the fraud are often unable to handle large-scale transaction data and evolving fraud patterns efficiently. This paper presents an AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions accurately and in the real time. To identify patterns and detect potential fraud, the proposed system analyzes past transaction data containing various transaction-related attributes amount, merchant details, category, time, and customer information. Data preprocessing techniques including K-Nearest Neighbor (KNN) imputation are applied to handle missing values, followed by label encoding and Z-score normalization to improve data quality and consistency. Feature engineering is performed to extract meaningful transaction patterns. The processed data is then classified using the XGBoost algorithm, which improves prediction accuracy through sequential learning and optimized decision trees. Experimental evaluation is carried out using a credit card transaction dataset containing legitimate and fraudulent records.

Keywords : Machine Learning, XGBoost, Credit Card Fraud Detection, K-Nearest Neighbor (KNN),Fraud Classification.

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