Predicting Credit Card Fraud using Supervised Machine Learning Methods: Comparative Analysis

Güner Altan () and Metin Recep Zafer
Additional contact information
Güner Altan: İstanbul-Türkiye
Metin Recep Zafer: İstanbul-Türkiye

Journal of Economic Policy Researches, 2024, vol. 11, issue 2, 242-262

Abstract: Currently, with the progress of technology, people’s and institutions’ range of expenditure channels via digital platforms has expanded. In addition, payment methods have become easier with the digital age. An expenditure, made from even a distant corner of the World, takes place instantaneously through the Internet. Although the rapid and global nature of digitisation contains many advantages, ensuring transaction security can be challenging. In this context, banks have undoubtedly become the most crucial institutions that mediate safe transactions between customers and sellers. In an era where credit card transactions are so prevalent, it is seen as a problem that needs to be solved by banks to determine whether these transactions involve fraud or not, both for their profitability and reputation. It takes a serious effort to determine that credit card expenditures, characterised by dynamic nature, are real expenses of the customer. Therefore, the aim of this study is to propose a model based on supervised machine learning with using real and current data with a few key features. The objective is to reduce banks’ operational burden and cost when identifying credit card fraud. In this context, the credit card transactions of a state-owned bank in January 2023 were considered, using a dataset comprising 13,050 observations. Python programming language is used for model building, and classification algorithms with high discriminatory power, such as Random Forest, Logistic Regression, K-Nearest Neighbours, Decision Trees, and Gradient Boosting, are preferred, which are machine learning techniques. The accuracy scores of the algorithms used in the model setup were determined as follows: Logistic Regression, 92.5%; Decision Tree, 93.1%; K-Nearest Neighbour 86.4%; Random Forest 91.8% and Gradient Boosting 86.9% and performance metrics, such as precision, recall, F1 score, and ROC-AUC, were also examined. Based on their performances, five algorithms were recommended for this study.

Keywords: Credit card fraud; Machine learning; Supervised learning; Random forest; Gradient boosting JEL Classification : C60; C69; C81 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/2B11EE1642DA46B7B55F9D37AAE8FBEF (application/pdf)
https://iupress.istanbul.edu.tr/en/journal/jepr/ar ... rsilastirmali-analiz (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ist:iujepr:v:11:y:2024:i:2:p:242-262

DOI: 10.26650/JEPR1433315

Access Statistics for this article

Journal of Economic Policy Researches is currently edited by Halil TUNALI

More articles in Journal of Economic Policy Researches from Istanbul University, Faculty of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Istanbul University Press Operational Team (Ertuğrul YAŞAR) ().