Credit Card Fraud Detection Using LSTM Algorithm
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions...
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| Format: | Article |
| Language: | English |
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College of Computer and Information Technology – University of Wasit, Iraq
2022-09-01
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| Series: | Wasit Journal of Computer and Mathematics Science |
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| Online Access: | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/60 |
| _version_ | 1797198932425572352 |
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| author | Yanash Azwin Mohmad |
| author_facet | Yanash Azwin Mohmad |
| author_sort | Yanash Azwin Mohmad |
| collection | DOAJ |
| description |
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.
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| first_indexed | 2024-03-07T19:03:27Z |
| format | Article |
| id | doaj.art-814b5cf28db84675b33da23c29d2867e |
| institution | Directory Open Access Journal |
| issn | 2788-5879 2788-5887 |
| language | English |
| last_indexed | 2024-04-24T07:07:43Z |
| publishDate | 2022-09-01 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| record_format | Article |
| series | Wasit Journal of Computer and Mathematics Science |
| spelling | doaj.art-814b5cf28db84675b33da23c29d2867e2024-04-21T18:57:32ZengCollege of Computer and Information Technology – University of Wasit, IraqWasit Journal of Computer and Mathematics Science2788-58792788-58872022-09-011310.31185/wjcm.60Credit Card Fraud Detection Using LSTM AlgorithmYanash Azwin Mohmad0Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring. https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/60Artificial intelligent, LSTM, Kolmogorov–Smirnov |
| spellingShingle | Yanash Azwin Mohmad Credit Card Fraud Detection Using LSTM Algorithm Wasit Journal of Computer and Mathematics Science Artificial intelligent, LSTM, Kolmogorov–Smirnov |
| title | Credit Card Fraud Detection Using LSTM Algorithm |
| title_full | Credit Card Fraud Detection Using LSTM Algorithm |
| title_fullStr | Credit Card Fraud Detection Using LSTM Algorithm |
| title_full_unstemmed | Credit Card Fraud Detection Using LSTM Algorithm |
| title_short | Credit Card Fraud Detection Using LSTM Algorithm |
| title_sort | credit card fraud detection using lstm algorithm |
| topic | Artificial intelligent, LSTM, Kolmogorov–Smirnov |
| url | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/60 |
| work_keys_str_mv | AT yanashazwinmohmad creditcardfrauddetectionusinglstmalgorithm |