Machine Learning Based Credit Card Fraud Detection System

Abstract

The rapid growth of online transactions and digital banking has significantly increased the use of credit cards for financial activities. However, this convenience has also led to a rise in credit card fraud, causing major financial losses for banks and customers. Traditional fraud detection systems mainly rely on rule-based methods, which are often inefficient in identifying new and complex fraud patterns. To address this challenge, this project proposes a Machine Learning Based Credit Card Fraud Detection System that automatically analyzes transaction data and identifies fraudulent activities with high accuracy.The system utilizes modern technologies such as Python for backend development and machine learning algorithms to analyze transaction patterns and detect anomalies. Transaction data is first preprocessed to remove noise and extract important features such as transaction amount, time, and location. Machine learning algorithms like Logistic Regression, Random Forest, and Isolation Forest are then applied to classify transactions as legitimate or fraudulent. The proposed system aims to improve fraud detection accuracy, reduce financial losses, and enhance the security of online transactions. By leveraging data-driven techniques and intelligent algorithms, the system can identify suspicious activities in real time and assist financial institutions in preventing fraudulent transactions. Experimental results show that the machine learning model can effectively detect fraudulent behavior and improve the efficiency of fraud detection systems.Furthermore, the system provides a scalable architecture for handling large volumes of transaction data and supports faster decision-making in financial security systems. By integrating machine learning with modern data processing techniques, the proposed system offers a reliable and efficient solution for credit card fraud detection in digital payment environments.

Introduction

This paper focuses on developing a machine learning-based credit card fraud detection system to address the growing risk of fraud in digital payment and online banking systems. While credit cards offer convenience, increasing transaction volume has also led to more sophisticated fraudulent activities that traditional rule-based systems struggle to detect.

The study proposes an intelligent system that uses machine learning models such as Logistic Regression, Random Forest, and Isolation Forest to analyze transaction features like amount, time, location, and spending behavior. These models help classify transactions as legitimate or fraudulent with higher accuracy and efficiency than manual or rule-based approaches.

The system is implemented using a three-tier architecture: a Python backend for processing and model prediction, a frontend interface for user interaction, and a Supabase database for secure data storage. It includes modules for transaction input, real-time analysis, fraud classification, alert generation, and result visualization.

Evaluation results show that the system effectively detects fraudulent transactions with strong performance across metrics such as accuracy, precision, recall, and F1-score. Visual dashboards and alerts further help users and financial institutions monitor suspicious activities and take preventive actions.

Conclusion

The proposed system integrates a Supabase database, Python backend, and Next.js frontend to create a scalable and efficient web-based fraud detection platform. The system processes transaction data and extracts important features such as transaction amount, time, frequency, and behavioral patterns using Python-based data preprocessing techniques. These features are then used by the machine learning model to analyze transaction behavior and identify potential fraud. A classification model, implemented using algorithms such as Logistic Regression and Random Forest, is used to detect suspicious transactions and classify them as either legitimate or fraudulent. The system generates a fraud risk score that helps determine the likelihood of fraudulent activity in a transaction. The results are displayed through an interactive user interface, where transaction details, fraud alerts, and prediction outcomes are visualized using charts and dashboards to improve interpretability. Experimental testing using transaction datasets demonstrates that the system can effectively analyze transaction patterns and identify potential fraud cases with good accuracy. Overall, the proposed solution provides financial institutions with a practical tool for monitoring transactions, detecting fraudulent activities, and enhancing the security of digital payment systems.

References

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Copyright

Copyright © 2026 Suravarapu Vijaya Ravi Kiran Naga Teja, Cherukuri Shankara Narasimha Naidu , Pilla Sravan, Dasari Eswara Srinivas, R. D. Bhanu Prakash. 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.