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Credit Card Fraud Prediction using Machine Learning

by Tushar Singh, Syed Wajahat Abbas Rizvi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 5
Year of Publication: 2025
Authors: Tushar Singh, Syed Wajahat Abbas Rizvi
10.5120/ijca2025924859

Tushar Singh, Syed Wajahat Abbas Rizvi . Credit Card Fraud Prediction using Machine Learning. International Journal of Computer Applications. 187, 5 ( May 2025), 24-29. DOI=10.5120/ijca2025924859

@article{ 10.5120/ijca2025924859,
author = { Tushar Singh, Syed Wajahat Abbas Rizvi },
title = { Credit Card Fraud Prediction using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 5 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number5/credit-card-fraud-prediction-using-machine-learning/ },
doi = { 10.5120/ijca2025924859 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-29T00:02:58.414732+05:30
%A Tushar Singh
%A Syed Wajahat Abbas Rizvi
%T Credit Card Fraud Prediction using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 5
%P 24-29
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increasing reliance on credit cards as a primary mode of payment has led to a significant rise in fraudulent transactions, making it imperative to develop robust fraud detection systems. Traditional methods of detecting fraud have proven inadequate in keeping up with the evolving tactics of fraudsters. This paper explores the application of machine learning techniques to predict and prevent credit card fraud. By leveraging a combination of supervised learning algorithms, such as Decision Trees, Random Forest, and Neural Networks, we aim to develop a model that accurately identifies fraudulent activities in real-time. The study also emphasizes the importance of data preprocessing, feature selection, and the use of appropriate evaluation metrics to enhance model performance. Our results demonstrate the effectiveness of machine learning models in detecting fraud with high accuracy, providing a scalable solution to mitigate financial risks for both consumers and financial institutions.

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Index Terms

Computer Science
Information Sciences

Keywords

Fraud detection Machine Learning Credit Card Prediction Model Financial Security Supervised Learning

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