Credit risk prediction in an imbalanced social lending environment
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best re...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2018-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25894605/view |
| _version_ | 1818040873261203456 |
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| author | Anahita Namvar Mohammad Siami Fethi Rabhi Mohsen Naderpour |
| author_facet | Anahita Namvar Mohammad Siami Fethi Rabhi Mohsen Naderpour |
| author_sort | Anahita Namvar |
| collection | DOAJ |
| description | Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets. |
| first_indexed | 2024-12-10T08:21:26Z |
| format | Article |
| id | doaj.art-95eb2ba46f6e434891ebd95813161a5f |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T08:21:26Z |
| publishDate | 2018-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-95eb2ba46f6e434891ebd95813161a5f2022-12-22T01:56:20ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832018-01-0111110.2991/ijcis.11.1.70Credit risk prediction in an imbalanced social lending environmentAnahita NamvarMohammad SiamiFethi RabhiMohsen NaderpourCredit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.https://www.atlantis-press.com/article/25894605/viewRisk predictionpeer-to-peer lendingimbalance classificationresampling |
| spellingShingle | Anahita Namvar Mohammad Siami Fethi Rabhi Mohsen Naderpour Credit risk prediction in an imbalanced social lending environment International Journal of Computational Intelligence Systems Risk prediction peer-to-peer lending imbalance classification resampling |
| title | Credit risk prediction in an imbalanced social lending environment |
| title_full | Credit risk prediction in an imbalanced social lending environment |
| title_fullStr | Credit risk prediction in an imbalanced social lending environment |
| title_full_unstemmed | Credit risk prediction in an imbalanced social lending environment |
| title_short | Credit risk prediction in an imbalanced social lending environment |
| title_sort | credit risk prediction in an imbalanced social lending environment |
| topic | Risk prediction peer-to-peer lending imbalance classification resampling |
| url | https://www.atlantis-press.com/article/25894605/view |
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