International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Analysis of Different Machine Learning Models for Credit Card Fraud Detection Harsh Mehta School of Computer Science, Presidency University, (UGC), Bangalore, Karnataka, India --------------------------------------------------------------------------***----------------------------------------------------------------------breach, skimming devices, or phishing attacks. These Abstract— The increase in number of online
scammers then use the stolen information to make unauthorized purchases or even cash withdrawals, often resulting in financial losses for cardholder and merchants. The problem goes beyond the loss of money as it affects the trust in digital payment systems, and potentially leads to long term economic instability if left unchecked.
transactions has led to a significant amount of credit card fraud over the past decade. Unauthorized use of one’s credit card information by stealing the information through dark web or scam calls, poses a major risk to both customer and businesses, particularly in e-commerce setting. This paper presents a comparative analysis of multiple machine learning models for credit card fraud detection, including logistic regression, isolation forest, K – mean clustering, and convolutional neural networks. With a highly unbalanced dataset we aim to evaluate these models’ performance in differentiating between genuine and fraudulent transactions based on features such as transaction history, user details, and merchant information. Our experiment results will help provide insights into effectiveness of each model for finding patterns to distinguish between real and fake that can be applied to real world data. This research contributes to the field of financial security by offering guidance on model selection for credit card fraud detection and related applications. View this project here.
B. Current challenges in detection The detection and prevention of credit card fraud presents several challenges for developers and organizations trying to deal with it. One of the primary obstacles is working with high dynamic nature of fraudulent activities, with scammers always changing and adapting new methods to cheat the detection system. This makes it necessary to keep evolving our detection methods to stay ahead of emerging threats and avoid before it even takes place. The number of genuine transactions vastly outnumber fraudulent one, this results in having a dataset where fraud transactions represent very minute number of the whole dataset. This imbalance creates biased models that prioritize the majority class, which might miss critical fraud transaction. Additionally, the sensitive nature of financial data often limits access to real world datasets, making it very difficult for researchers and developers to build and test a model.
Keywords – Credit card fraud, machine learning, logistic regression, isolation forest, k-mean clustering, convolutional neural network, financial security.
I.
INTRODUCTION
C. Our approach and its significance
The rapid growth of online financial transactional methods are seen in the recent times and adopted widely because it’s easy, reliable, and faster in multiple aspects compared to traditional payment methods. Among this online credit card fraud has been a concerning issue that challenges the security and integrity of information that can be circulated through internet. This paper will help future peers in understanding and choosing models according to their build requirements.
Our approach to address this issue involves a performance analysis of multiple machine learning model applied to credit card fraud detection. Using a dataset from Kaggle named “Credit Card Transactions Fraud Detection Dataset” (Brandon, 2022) which mimics real world transaction pattern while preserving user’s privacy, we implemented a unique methodology where we evaluate the effectiveness of different models like: regression model, decision tree model, clustering model and convolutional neural network (CNN). We compare the performance of these models across multiple metrics, such as classification report, confusion metrics, AUC-ROC scores and feature importance analysis, through this we aim to find relative strengths and weaknesses in the context of credit card fraud detection. This performance evaluation contributes to providing help in ongoing efforts for improvement in fraud detection systems and offers valuable guidance to future peers in selecting and implementing appropriate model according to needs for similar security applications.
A. Background on credit card frauds Credit card frauds have become a significant threat in the coming digital age, possessing an enormous financial risk to individual, businesses and the global financial system. As e-commerce and digital transactions grow with time so does the fraudulent activities. Credit card fraud generally occurs when unauthorized individual gain access to card information through various means like data
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