Reassessment and Monitoring of Loan Applications with Machine Learning
Credit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. W...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-11-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2018.1525517 |
| _version_ | 1797684919419273216 |
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| author | Zeynep Boz Dilek Gunnec S. Ilker Birbil M. Kaan Öztürk |
| author_facet | Zeynep Boz Dilek Gunnec S. Ilker Birbil M. Kaan Öztürk |
| author_sort | Zeynep Boz |
| collection | DOAJ |
| description | Credit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. We use a real data set from one of the prominent loan companies in Turkey. The information provided by the applicants form the variables in our analysis. The company’s experts have already labeled the clients as bad and good according to their ongoing payments. Using this labeled data set, we execute several methods to classify the bad applicants as well as the significant variables in this classification. As the data set consists of applicants who have passed the initial scoring system, most of the clients are marked as good. To deal with this imbalanced nature of the problem, we employ a set of different approaches to improve the performance of predicting the applicants who are likely to default. In the second part of this study, we aim to predict the payment behavior of clients based on their static (demographic and financial) and dynamic (payment) information. Furthermore, we analyze the effect of the length of the payment history and the staying power of the proposed prediction models. |
| first_indexed | 2024-03-12T00:36:46Z |
| format | Article |
| id | doaj.art-ff5ad806f9014d9b9cd92bbd2e34d6f5 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:46Z |
| publishDate | 2018-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-ff5ad806f9014d9b9cd92bbd2e34d6f52023-09-15T09:33:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452018-11-01329-1093995510.1080/08839514.2018.15255171525517Reassessment and Monitoring of Loan Applications with Machine LearningZeynep Boz0Dilek Gunnec1S. Ilker Birbil2M. Kaan Öztürk3Sabancı UniversityÖzyeğin UniversityErasmus University RotterdamBoğaziçi UniversityCredit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. We use a real data set from one of the prominent loan companies in Turkey. The information provided by the applicants form the variables in our analysis. The company’s experts have already labeled the clients as bad and good according to their ongoing payments. Using this labeled data set, we execute several methods to classify the bad applicants as well as the significant variables in this classification. As the data set consists of applicants who have passed the initial scoring system, most of the clients are marked as good. To deal with this imbalanced nature of the problem, we employ a set of different approaches to improve the performance of predicting the applicants who are likely to default. In the second part of this study, we aim to predict the payment behavior of clients based on their static (demographic and financial) and dynamic (payment) information. Furthermore, we analyze the effect of the length of the payment history and the staying power of the proposed prediction models.http://dx.doi.org/10.1080/08839514.2018.1525517 |
| spellingShingle | Zeynep Boz Dilek Gunnec S. Ilker Birbil M. Kaan Öztürk Reassessment and Monitoring of Loan Applications with Machine Learning Applied Artificial Intelligence |
| title | Reassessment and Monitoring of Loan Applications with Machine Learning |
| title_full | Reassessment and Monitoring of Loan Applications with Machine Learning |
| title_fullStr | Reassessment and Monitoring of Loan Applications with Machine Learning |
| title_full_unstemmed | Reassessment and Monitoring of Loan Applications with Machine Learning |
| title_short | Reassessment and Monitoring of Loan Applications with Machine Learning |
| title_sort | reassessment and monitoring of loan applications with machine learning |
| url | http://dx.doi.org/10.1080/08839514.2018.1525517 |
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