Published 2025-04-14
Keywords
- Fintech,
- credit card fraud detection,
- ensemble learning,
- , machine learning,
- simulated data set
- real-world data set ...More
How to Cite
Copyright (c) 2025 P Swathi, K Ajtih Kumar, S Shaikshavali, P Jyoshna, T Anusha

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
Detecting fraudulent credit card transactions is a significant challenge for financial institutions, as cybercriminals often impersonate legitimate users to conduct unauthorized transactions. Since every approved transaction requires verification, effective fraud detection mechanisms are essential. Ensemble learning techniques can enhance fraud detection by analyzing both synthetic and real-world consumer transaction datasets. Models such as XGBoost, random forests, and naive Bayes classifiers are tested, with performance evaluated based on accuracy, precision, recall, and F1 score. Results show that ensemble classifiers perform well on real transaction data but struggle with synthetic datasets. Real-world transaction systems adapt quickly to structured patterns, improving fraud detection effectiveness. However, the deterministic nature of these systems, combined with minimal randomness, may increase the vulnerability of credit card information.
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