Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1071-1075· 0 citations· 15 references
Abstract
For identifying fraud within a vast array of digitally conducted financial transactions, it is imperative for financial transaction systems to be scalable and accurate. One of the common uses of machine learning in the financial sector is credit card fraud detection, particularly when manual monitoring of the massive volume of financial transactions is impractical. The proposed system is designed using the XGBoost classifier. This approach improves the ability to detect fraudulent credit card transactions through various behavioral and geographical aspects, including transaction time, transaction frequency, and the distance between the cardholder and merchant locations. Experimental results indicate that the proposed system is capable of detecting fraudulent transactions with a precision of approximately 99.8% and a high ROC-AUC score. The scalable framework designed in this study can assist financial organizations in improving the security and effectiveness of fraud detection systems.
An AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions accurately and in the real time and improves prediction accuracy through sequential learning and optimized decision trees.
P. Ravikumar, Gowrav A. S., A. N et al.· International Journal of Inn...· 0 citations
Due to the rapid advancement of digital payment tools, the number of credit card fraud cases is increasing at a faster rate, posing a greater risk to users and financial institutions and highlighting the flaws of traditional rule-based preventive measures. The greatest challenge in this area is that transaction data is...
Tejaswini H, Vamshi K· International Conference Com...· 0 citations
The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.
Jabulani Khumalo, Min Joon Kim· Global Knowledge Academy· 0 citations
Comparative analysis demonstrates that ensemble learning methods, particularly Random Forest and XGBoost, outperform traditional classification techniques by achieving higher detection rates and lower false positive rates.
Dr. Abdul Majid Farooqi Dr. Abdul Majid Farooqi, Laiba Khan LAIBA KHAN· International Scientific Jou...· 0 citations
The rapid evolution of digital banking and e-commerce puts online payment fraud problems at another level. Because of the large class imbalance present, coupled with the ever-changing nature of fraud, in the instance of credit card fraud, it becomes doubly challenging to achieve real-time detection. Fraudsters continuo...
Firdos R. Kachur, Shivangi Agarwal, Gargi Phadke· International Conference Com...· 0 citations
A comprehensive, real-time credit card fraud detection framework that integrates geospatial and occupational user behavior features with supervised machine learning models is proposed and successfully bridges the gap between theoretical machine learning models and practical, low-latency deployment in financial technolo...
Narayana Reddy T, Arun Kumar T M, Nagaraju V et al.· American Journal of AI Cyber...· 0 citations
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