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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings
EditorsMonica Andrea Rico Martinez
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147468
DOIs
StatePublished - Oct 2019
Event2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019 - Bogota, Colombia
Duration: 2 Oct 20194 Oct 2019

Publication series

Name2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings

Conference

Conference2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019
CountryColombia
CityBogota
Period2/10/194/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Analysis algorithms
  • Bayesian networks
  • Credit Scoring
  • Data analysis
  • Microfinance
  • Moroseness
  • risk

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