Scoring de crédito y gestión del riesgo en créditos de consumo: análisis documental de la Cooperativa de Ahorro y Crédito 13 de Abril, Ventanas, Ecuador (2026)
DOI:
https://doi.org/10.65415/p2qs9s36Palabras clave:
scoring de crédito; riesgo crediticio; crédito de consumo; cooperativas de ahorro y crédito; morosidad; gestión financieraResumen
El estudio analiza el papel del scoring de crédito en la gestión del riesgo de la línea de consumo de la Cooperativa de Ahorro y Crédito 13 de Abril, ubicada en Ventanas, Ecuador, durante 2026. Se adoptó un diseño no experimental, transversal y descriptivo-analítico, sustentado en análisis documental del ensayo original, la ficha institucional del producto de crédito de consumo, normativa ecuatoriana y literatura científica especializada. Se examinaron las condiciones del producto, los requisitos documentales, los criterios de evaluación, los niveles de aprobación y los mecanismos de seguimiento, contrastándolos con principios contemporáneos de credit scoring y gestión del riesgo. Los resultados muestran que la cooperativa dispone de un proceso estructurado basado en capacidad de pago, ingresos, historial crediticio, endeudamiento, garantías y revisión por instancias de aprobación; sin embargo, la documentación analizada no evidencia un modelo formal de puntuación, probabilidad de incumplimiento, umbrales de decisión, validación predictiva ni monitoreo de desempeño del modelo. En consecuencia, existe una base informacional aprovechable para evolucionar hacia un scoring trazable y explicable. La ausencia de microdatos de cartera impide estimar estadísticamente su efecto sobre morosidad o recuperación, por lo que los hallazgos constituyen un diagnóstico documental orientado a fortalecer futuras decisiones crediticias con evidencia institucional.
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Ayari, H., Guetari, R., & Kraïem, N. (2026). Machine learning powered financial credit scoring: A systematic literature review. Artificial Intelligence Review, 59, Article 13. https://doi.org/10.1007/s10462-025-11416-2
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.
Basel Committee on Banking Supervision. (2025). Principles for the management of credit risk. Bank for International Settlements. https://www.bis.org/bcbs/publ/d595.htm
Bequé, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications, 86, 42–53. https://doi.org/10.1016/j.eswa.2017.05.050
Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: A systematic literature review. Journal of Banking and Financial Technology, 4(1), 111–138. https://doi.org/10.1007/s42786-020-00020-3
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033
Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2022). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society, 73(1), 70–90. https://doi.org/10.1080/01605682.2021.1922098
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216. https://doi.org/10.1007/s10614-020-10042-0
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024). Interpretable machine learning for imbalanced credit scoring datasets. European Journal of Operational Research, 312(1), 357–372. https://doi.org/10.1016/j.ejor.2023.06.036
Cooperativa de Ahorro y Crédito 13 de Abril. (2026). Características de productos de crédito: Crédito de consumo [Documento institucional no publicado].
Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465. https://doi.org/10.1016/j.ejor.2006.09.100
Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263. https://doi.org/10.1016/j.asoc.2020.106263
Djeundje, V. B., Crook, J., Calabrese, R., & Hamid, M. (2021). Enhancing credit scoring with alternative data. Expert Systems with Applications, 163, 113766. https://doi.org/10.1016/j.eswa.2020.113766
Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. (2022). Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, 297(3), 1178–1192. https://doi.org/10.1016/j.ejor.2021.06.053
Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523–541. https://doi.org/10.1111/j.1467-985X.1997.00078.x
Jammalamadaka, K. R., & Itapu, S. (2023). Responsible AI in automated credit scoring systems. AI and Ethics, 3(2), 485–495. https://doi.org/10.1007/s43681-022-00175-3
Kozodoi, N., Jacob, J., & Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. European Journal of Operational Research, 297(3), 1083–1094. https://doi.org/10.1016/j.ejor.2021.06.023
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117–134. https://doi.org/10.1016/j.sorms.2016.10.001
Montevechi, A. A., de Carvalho Miranda, R., Medeiros, A. L., & Montevechi, J. A. B. (2024). Advancing credit risk modelling with machine learning: A comprehensive review of the state-of-the-art. Engineering Applications of Artificial Intelligence, 137, 109082. https://doi.org/10.1016/j.engappai.2024.109082
Noriega, J. P., Rivera, L. A., & Herrera, J. A. (2023). Machine learning for credit risk prediction: A systematic literature review. Data, 8(11), 169. https://doi.org/10.3390/data8110169
Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. (2022). Machine learning-driven credit risk: A systemic review. Neural Computing and Applications, 34(17), 14327–14339. https://doi.org/10.1007/s00521-022-07472-2
Suhadolnik, N., Ueyama, J., & Da Silva, S. (2023). Machine learning for enhanced credit risk assessment: An empirical approach. Journal of Risk and Financial Management, 16(12), 496. https://doi.org/10.3390/jrfm16120496
Superintendencia de Economía Popular y Solidaria. (2023). Norma para la gestión del riesgo de crédito en las cooperativas de ahorro y crédito y asociaciones mutualistas de ahorro y crédito para la vivienda (Resolución Nro. SEPS-IGT-IGS-INSESF-INR-INGINT-2023-0225).
Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172. https://doi.org/10.1016/S0169-2070(00)00034-0
Zhang, X., & Yu, L. (2024). Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods. Expert Systems with Applications, 237, 121484. https://doi.org/10.1016/j.eswa.2023.121484
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Derechos de autor 2026 Adriana Carolina Ceruffo Rivera, Maria Elena Carriel Cabrera
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
