Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms
This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to un...
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MDPI AG
2025-02-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/7/1/20 |
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| author | Kianeh Kandi Antonio García-Dopico |
| author_facet | Kianeh Kandi Antonio García-Dopico |
| author_sort | Kianeh Kandi |
| collection | DOAJ |
| description | This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampling Technique (SMOTE) to address any imbalance in class distribution. This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. The comparison of LSTM and XGBoost shows that LSTM is more effective for our imbalanced dataset. Compared with XGBOOST’s 97% accuracy, LSTM’s accuracy is 99%. The final result emphasizes how crucial it is to select the optimal algorithm based on particular criteria within financial concerns, which will ultimately result in more reliable and knowledgeable credit score decisions. |
| format | Article |
| id | doaj-art-05646446f3a04cbfae1644c22e511c4d |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-05646446f3a04cbfae1644c22e511c4d2025-08-20T01:49:00ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902025-02-01712010.3390/make7010020Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost AlgorithmsKianeh Kandi0Antonio García-Dopico1Departamento de Arquitectura y Tecnología de Sistemas Informáticos (DATSI) Computer Science, Escuelta Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, SpainDepartamento de Arquitectura y Tecnología de Sistemas Informáticos (DATSI) Computer Science, Escuelta Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, SpainThis research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampling Technique (SMOTE) to address any imbalance in class distribution. This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. The comparison of LSTM and XGBoost shows that LSTM is more effective for our imbalanced dataset. Compared with XGBOOST’s 97% accuracy, LSTM’s accuracy is 99%. The final result emphasizes how crucial it is to select the optimal algorithm based on particular criteria within financial concerns, which will ultimately result in more reliable and knowledgeable credit score decisions.https://www.mdpi.com/2504-4990/7/1/20recurrent neural network (RNN)long short-term memory (LSTM) networkextreme gradient boosting (XGBoost)synthetic minority oversampling technique (SMOTE)imbalanced dataset |
| spellingShingle | Kianeh Kandi Antonio García-Dopico Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms Machine Learning and Knowledge Extraction recurrent neural network (RNN) long short-term memory (LSTM) network extreme gradient boosting (XGBoost) synthetic minority oversampling technique (SMOTE) imbalanced dataset |
| title | Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms |
| title_full | Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms |
| title_fullStr | Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms |
| title_full_unstemmed | Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms |
| title_short | Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms |
| title_sort | enhancing performance of credit card model by utilizing lstm networks and xgboost algorithms |
| topic | recurrent neural network (RNN) long short-term memory (LSTM) network extreme gradient boosting (XGBoost) synthetic minority oversampling technique (SMOTE) imbalanced dataset |
| url | https://www.mdpi.com/2504-4990/7/1/20 |
| work_keys_str_mv | AT kianehkandi enhancingperformanceofcreditcardmodelbyutilizinglstmnetworksandxgboostalgorithms AT antoniogarciadopico enhancingperformanceofcreditcardmodelbyutilizinglstmnetworksandxgboostalgorithms |