Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model
Credit score is the basis for financial institutions to make credit decisions. With the development of science and technology, big data technology has penetrated into the financial field, and personal credit investigation has entered a new era. Personal credit evaluation based on big data is one of...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9222617 |
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| _version_ | 1849685105008705536 |
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| author | Yi Wu Yuwen Pan |
| author_facet | Yi Wu Yuwen Pan |
| author_sort | Yi Wu |
| collection | DOAJ |
| description | Credit score is the basis for financial institutions to make credit decisions. With the development of science and technology, big data technology has penetrated into the financial field, and personal credit investigation has entered a new era. Personal credit evaluation based on big data is one of the hot research topics. This paper mainly completes three works. Firstly, according to the application scenario of credit evaluation of personal credit data, the experimental dataset is cleaned, the discrete data is one-HOT coded, and the data are standardized. Due to the high dimension of personal credit data, the pdC-RF algorithm is adopted in this paper to optimize the correlation of data features and reduce the 145-dimensional data to 22-dimensional data. On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. It is found that logistic regression is more suitable for the personal credit evaluation model based on Lending Club dataset. Finally, based on the logistic regression model with the best parameters, the user samples are graded and the final score card is output. |
| format | Article |
| id | doaj-art-b9a4c1ecd5d243bab4570a0f3bb15437 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b9a4c1ecd5d243bab4570a0f3bb154372025-08-20T03:23:15ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/92226179222617Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning ModelYi Wu0Yuwen Pan1Emlyon Business School, Ecully Cedex 69134, FranceEmlyon Business School, Ecully Cedex 69134, FranceCredit score is the basis for financial institutions to make credit decisions. With the development of science and technology, big data technology has penetrated into the financial field, and personal credit investigation has entered a new era. Personal credit evaluation based on big data is one of the hot research topics. This paper mainly completes three works. Firstly, according to the application scenario of credit evaluation of personal credit data, the experimental dataset is cleaned, the discrete data is one-HOT coded, and the data are standardized. Due to the high dimension of personal credit data, the pdC-RF algorithm is adopted in this paper to optimize the correlation of data features and reduce the 145-dimensional data to 22-dimensional data. On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. It is found that logistic regression is more suitable for the personal credit evaluation model based on Lending Club dataset. Finally, based on the logistic regression model with the best parameters, the user samples are graded and the final score card is output.http://dx.doi.org/10.1155/2021/9222617 |
| spellingShingle | Yi Wu Yuwen Pan Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model Complexity |
| title | Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model |
| title_full | Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model |
| title_fullStr | Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model |
| title_full_unstemmed | Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model |
| title_short | Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model |
| title_sort | application analysis of credit scoring of financial institutions based on machine learning model |
| url | http://dx.doi.org/10.1155/2021/9222617 |
| work_keys_str_mv | AT yiwu applicationanalysisofcreditscoringoffinancialinstitutionsbasedonmachinelearningmodel AT yuwenpan applicationanalysisofcreditscoringoffinancialinstitutionsbasedonmachinelearningmodel |