Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 2 , Issue 1 , PP: 46-54, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Khadija Shazly 1 * , Nima Khodadadi 2
Doi: https://doi.org/10.54216/JAIM.020105
Credit card use is rapidly increasing as a result of the widespread availability of these cards, the ease of making electronic transfers, and the ubiquity of online shopping. But credit card debt poses a serious risk to businesses and governments alike, not to mention individual savers and investors. Consequently, the need for efficient, timely, and reliable ways to anticipate credit card risk has grown. In this study, we offer a framework that combines three classifiers, namely, support vector machines, multilayer perceptron and decision trees, to improve the network's accuracy. The proposed strategy is shown to be very competitive with others through simulation.
Credit scoring , Credit card , Machine learning , Classification , Metaheuristic optimization , K-Nearest neighbor , Random Forest , Support vector machines
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