Credit Risk Assessment Models in Financial Technology: A Review

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dc.creatorTadeo Espinoza, Frank Edward
dc.creatorCoral Ygnacio, Marco Antonio
dc.date2023-12-20
dc.date.accessioned2025-10-01T23:53:09Z
dc.descriptionThis review analyzes a selection of scientific articles on the implementation of Credit Risk Assessment (CRA) systems to identify existing solutions, the most accurate ones, and limitations and problems in their development. The PRISMA statement was adopted as follows: the research questions were formulated, the inclusion criteria were defined, the keywords were selected, and the search string was designed. Finally, several descriptive statistics of the selected articles were calculated. Thirty-one solutions were identified in the selected studies; they include methods, models, and algorithms. Some of the most widely used models are based on Artificial Intelligence (AI) techniques, especially Neural Networks and Random Forest. It was concluded that Neural Networks are the most efficient solutions, with average accuracies above 90 %, but their development can have limitations. These solutions should be implemented considering the context in which they will be employed.en-US
dc.descriptionEsta revisión analiza una selección de artículos científicos sobre la implantación de sistemas de evaluación del riesgo de crédito para identificar las soluciones existentes, las más acertadas y las limitaciones y problemas en su desarrollo. Se adoptó la declaración PRISMA del siguiente modo: se formularon las preguntas de investigación, se definieron los criterios de inclusión, se seleccionaron las palabras clave y se diseñó la cadena de búsqueda. Por último, se calcularon varios estadísticos descriptivos de los artículos seleccionados. En los estudios seleccionados se identificaron 31 soluciones, entre métodos, modelos y algoritmos. Algunos de los modelos más utilizados se basan en técnicas de Inteligencia Artificial (IA), especialmente Redes Neuronales y Bosques Aleatorios. Se concluyó que las Redes Neuronales son las soluciones más eficientes, con precisiones medias superiores al 90 %, pero su desarrollo puede tener limitaciones. Estas soluciones deben implementarse teniendo en cuenta el contexto en el que se van a emplear.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679
dc.identifier10.22430/22565337.2679
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7873
dc.languageeng
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679/2994
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679/3090
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679/3091
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679/3108
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2679/3415
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dc.rightsDerechos de autor 2023 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 26 No. 58 (2023); e2679en-US
dc.sourceTecnoLógicas; Vol. 26 Núm. 58 (2023); e2679es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectcredit assessmenten-US
dc.subjectcredit risken-US
dc.subjecttechnology solutionsen-US
dc.subjectmachine learningen-US
dc.subjectalgorithmsen-US
dc.subjectevaluación crediticiaes-ES
dc.subjectriesgo de créditoes-ES
dc.subjectsoluciones tecnológicases-ES
dc.subjectaprendizaje automáticoes-ES
dc.subjectalgoritmoses-ES
dc.titleCredit Risk Assessment Models in Financial Technology: A Reviewen-US
dc.titleModelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisiónes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeReview Articleen-US
dc.typeArtículos de revisiónes-ES

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