Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks

Eduardo Alarcon Morales, Brian Mora Ramos, Jimmy Armas Aguirre, David Mauricio Sanchez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

In this paper, we propose a risk analysis model to obtain the probability of default of microfinance clients in Peru. Our model uses trends of predictive analysis through variants of neural network algorithms; and data processing methodologies such as the Knowledge Discovery in Databases (KDD). The analysis method is used through Bayesian networks which allows the customer data evaluation and is related to our model data. This model is composed of 5 phases: 1. The input elements for the analysis; 2. The process of evaluation and analysis; 3. The regulatory standards; 4. The technological architecture; 5. The output elements. This model allows knowing the probability of compliance of a client with 84% prediction accuracy. The model validation was carried out in a microfinance institution in Lima, Peru, using cross-validation, evaluating the sensitivity and specificity of the results.

Idioma originalInglés
Título de la publicación alojada2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings
EditoresMonica Andrea Rico Martinez
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728147468
DOI
EstadoPublicada - oct 2019
Evento2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019 - Bogota, Colombia
Duración: 2 oct 20194 oct 2019

Serie de la publicación

Nombre2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings

Conferencia

Conferencia2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019
País/TerritorioColombia
CiudadBogota
Período2/10/194/10/19

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© 2019 IEEE.

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