Fraud Detection in Credit Card Payments and Payment Gateways Using Machine Learning

Abstract

Credit card fraud has become a very common problem in the modern economy, especially with the rise of online transactions. This paper investigates advanced machine learning techniques for fraud detection, using models such as Random Forest, Gradient Boosting, Support Vector Machines (SVM), and Long Short-Term Memory Networks (LSTM). A hybrid ensemble model is also proposed to enhance the accuracy of detection while reducing false positives and negatives. The experimental results have proven the efficiency of these models, and the hybrid model performed better. [1] [2]

Introduction

Credit cards are widely used for online and offline transactions, offering users short-term credit and convenience. However, credit card fraud has risen significantly due to evolving technologies and vulnerabilities in both physical (POS) and online transactions. Fraud occurs when a card is used without the owner’s permission for financial gain. Common fraud types include POS fraud using hidden skimming devices, card skimming to clone magnetic-stripe data, and phishing schemes that trick cardholders into revealing personal information. Reports such as the NCRB 2020 highlight a major increase in fraud cases, emphasizing the need for stronger detection and prevention systems.

To combat these threats, machine learning techniques are employed to identify suspicious behavior and flag fraudulent activities. Several algorithms support fraud detection:

• XGBoost: An efficient gradient-boosting method that builds a sequence of decision trees and uses regularization to prevent overfitting.

• LSTM networks: A form of recurrent neural network capable of recognizing temporal patterns in transaction sequences.

• Random Forest: An ensemble of decision trees that improves accuracy and reduces variance through random sampling of data and features.

• SVM: A supervised learning model that identifies an optimal hyperplane to classify transactions as legitimate or fraudulent.

• Hybrid/Ensemble Models: Combine multiple algorithms to enhance robustness and accuracy.

  • Stacking integrates several base models (e.g., Random Forest, Gradient Boosting, SVM) and uses a meta-model, often Logistic Regression, to improve final predictions.

References

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Copyright

Copyright © 2025 Dhanashree Patil, Atharv Bandewar, Bhavesh Chaure, Shubham Adhav, Abhijeet Date. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.