Abstract
The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.
Highlights
The growth of the internet has led to a significant rise in credit card usage
The WEKA (Waikato environment for knowledge analysis) tool is used for Support Vector Machine (SVM) and Logistic Regression to calculate the efficiency based on accuracy garnered from the confusion matrix and Python programming language is developed for Deep Learning (DL)
As the deep learning results depend on the initial parameters, the algorithm was run for 5 times and the results reported in Table V are the average results of the five experimentations
Summary
The growth of the internet has led to a significant rise in credit card usage. It is one of the most used payment methods these days. The downturn of financial institutions in the USA and Europe during the US subprime mortgage and the European sovereign crisis has raised concerns about risk management properly [2] These challenges have attracted significant attention from researchers and practitioners. Most studies used traditional statistical, machine learning, and deep learning techniques to detect credit card fraud and compared the results [1]-[3], [5][7], [12], [40], [41]. It will build a deep learning model based on the best parameters for the credit card dataset. A comparative study between deep learning and traditional machine learning algorithms (Logistic Regression and SVM) will be conducted
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