Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances
This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk managemen...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/13/2/20 |
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| author | Ivan Mauricio Bermudez Vera Jaime Mosquera Restrepo Diego Fernando Manotas-Duque |
| author_facet | Ivan Mauricio Bermudez Vera Jaime Mosquera Restrepo Diego Fernando Manotas-Duque |
| author_sort | Ivan Mauricio Bermudez Vera |
| collection | DOAJ |
| description | This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk management by incorporating internal and external data to assess default risk. Data mining techniques are applied to address class imbalances, redefining the term “default” to include external credit information and increasing the representation of the minority class. The effectiveness of machine learning and statistical models is evaluated using class-balancing methods such as under-sampling, over-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation is based on the Balanced Accuracy metric and the holding power of the performance, ensuring a consistent predictive power of the model while avoiding overfitting. While machine learning methods can improve credit scoring, logistic regression-based models remain effective, especially when combined with class-balancing techniques. It is concluded that a balanced sample in a class size is essential to improve predictive performance. |
| format | Article |
| id | doaj-art-e28cf82803d44334bc2155b83532b894 |
| institution | DOAJ |
| issn | 2227-9091 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj-art-e28cf82803d44334bc2155b83532b8942025-08-20T02:44:56ZengMDPI AGRisks2227-90912025-01-011322010.3390/risks13020020Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class ImbalancesIvan Mauricio Bermudez Vera0Jaime Mosquera Restrepo1Diego Fernando Manotas-Duque2School of Industrial Engineering, Universidad del Valle, Cali 760042, ColombiaSchool of Statistics, Universidad del Valle, Cali 760042, ColombiaSchool of Industrial Engineering, Universidad del Valle, Cali 760042, ColombiaThis study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk management by incorporating internal and external data to assess default risk. Data mining techniques are applied to address class imbalances, redefining the term “default” to include external credit information and increasing the representation of the minority class. The effectiveness of machine learning and statistical models is evaluated using class-balancing methods such as under-sampling, over-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation is based on the Balanced Accuracy metric and the holding power of the performance, ensuring a consistent predictive power of the model while avoiding overfitting. While machine learning methods can improve credit scoring, logistic regression-based models remain effective, especially when combined with class-balancing techniques. It is concluded that a balanced sample in a class size is essential to improve predictive performance.https://www.mdpi.com/2227-9091/13/2/20credit risksolidarity economydata miningclass balancinglogistic regression |
| spellingShingle | Ivan Mauricio Bermudez Vera Jaime Mosquera Restrepo Diego Fernando Manotas-Duque Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances Risks credit risk solidarity economy data mining class balancing logistic regression |
| title | Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances |
| title_full | Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances |
| title_fullStr | Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances |
| title_full_unstemmed | Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances |
| title_short | Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances |
| title_sort | data mining for the adjustment of credit scoring models in solidarity economy entities a methodology for addressing class imbalances |
| topic | credit risk solidarity economy data mining class balancing logistic regression |
| url | https://www.mdpi.com/2227-9091/13/2/20 |
| work_keys_str_mv | AT ivanmauriciobermudezvera dataminingfortheadjustmentofcreditscoringmodelsinsolidarityeconomyentitiesamethodologyforaddressingclassimbalances AT jaimemosquerarestrepo dataminingfortheadjustmentofcreditscoringmodelsinsolidarityeconomyentitiesamethodologyforaddressingclassimbalances AT diegofernandomanotasduque dataminingfortheadjustmentofcreditscoringmodelsinsolidarityeconomyentitiesamethodologyforaddressingclassimbalances |