A Proposed Model for Credit Card Fraud Detection Model Using Machine Learning Technique

Harith Safwan Ezzulddin

doi:10.51173/ijds.v3i1.50

Authors

Harith Safwan Ezzulddin Department of Computer Sciences, University of Altinbas, Istanbul, Turkey

DOI:

https://doi.org/10.51173/ijds.v3i1.50

Keywords:

Credit Card Fraud Detection, Logistic Regression, Random Forest, ATM Fraud Activity Detection

Abstract

The online payment system is at high risk due to the increasing rates of credit card theft. The primary objective is to identify cases of credit card theft by analysing the purchase history of cardholders and categorising them accordingly. These include an increase in the slope of logistics, steep slope, and scattered woodlands. The proposed model utilises tools such as logistic regression and random forest as machine learning techniques. Additionally, a set of preprocessing techniques is employed, including data balancing using SMOTE. After being trained on a large dataset of credit card transactions, the model is used to detect trends and anomalies that may indicate fraudulent activity, taking into account factors such as transaction amount, location, and time of day. We have used artificial minority oversampling to put the data set into proper perspective. The two algorithms were applied, yielding 97.34% accuracy for Logistic Regression and 99.99% accuracy for Random Forest. The accuracy metric is used for performance evaluation. The results indicate a promising performance that can enhance credit card security, potentially helping to reduce financial losses to victims of fraud.

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