Credit card default prediction using ML and DL techniques
The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types...
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345224000087 |
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| author | Fazal Wahab Imran Khan Sneha Sabada |
| author_facet | Fazal Wahab Imran Khan Sneha Sabada |
| author_sort | Fazal Wahab |
| collection | DOAJ |
| description | The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. |
| format | Article |
| id | doaj-art-4c0431bb896144ac9d9f899c530af6e7 |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-4c0431bb896144ac9d9f899c530af6e72025-01-27T04:22:37ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-014293306Credit card default prediction using ML and DL techniquesFazal Wahab0Imran Khan1Sneha Sabada2Corvit Systems Peshawar, Pakistan; Corresponding author.Department of Informatics, University of Sussex, UK; Corresponding author.Department of Engineering and Computer Science, University of Hertfordshire, UKThe banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %.http://www.sciencedirect.com/science/article/pii/S2667345224000087Deep learningMachine learningCredit card default predictionAda boostDecision tree |
| spellingShingle | Fazal Wahab Imran Khan Sneha Sabada Credit card default prediction using ML and DL techniques Internet of Things and Cyber-Physical Systems Deep learning Machine learning Credit card default prediction Ada boost Decision tree |
| title | Credit card default prediction using ML and DL techniques |
| title_full | Credit card default prediction using ML and DL techniques |
| title_fullStr | Credit card default prediction using ML and DL techniques |
| title_full_unstemmed | Credit card default prediction using ML and DL techniques |
| title_short | Credit card default prediction using ML and DL techniques |
| title_sort | credit card default prediction using ml and dl techniques |
| topic | Deep learning Machine learning Credit card default prediction Ada boost Decision tree |
| url | http://www.sciencedirect.com/science/article/pii/S2667345224000087 |
| work_keys_str_mv | AT fazalwahab creditcarddefaultpredictionusingmlanddltechniques AT imrankhan creditcarddefaultpredictionusingmlanddltechniques AT snehasabada creditcarddefaultpredictionusingmlanddltechniques |