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

doi:10.1109/CONIITI48476.2019.8960694

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

Faculty of Engineering

Universidad Peruana de Ciencias Aplicadas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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 language	English
Title of host publication	2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings
Editors	Monica Andrea Rico Martinez
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728147468
DOIs	https://doi.org/10.1109/CONIITI48476.2019.8960694
State	Published - Oct 2019
Event	2019 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 2019 → 4 Oct 2019

Publication series

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

Conference

Conference	2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019
Country/Territory	Colombia
City	Bogota
Period	2/10/19 → 4/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 1 No Poverty
SDG 5 Gender Equality
SDG 8 Decent Work and Economic Growth

Keywords

Analysis algorithms
Bayesian networks
Credit Scoring
Data analysis
Microfinance
Moroseness
risk

Access to Document

10.1109/CONIITI48476.2019.8960694

Cite this

Morales, E. A., Ramos, B. M., Aguirre, J. A., & Sanchez, D. M. (2019). Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks. In M. A. R. Martinez (Ed.), 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings Article 8960694 (2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONIITI48476.2019.8960694

Morales, Eduardo Alarcon ; Ramos, Brian Mora ; Aguirre, Jimmy Armas et al. / Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks. 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings. editor / Monica Andrea Rico Martinez. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings).

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title = "Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks",

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.",

keywords = "Analysis algorithms, Bayesian networks, Credit Scoring, Data analysis, Microfinance, Moroseness, risk",

author = "Morales, \{Eduardo Alarcon\} and Ramos, \{Brian Mora\} and Aguirre, \{Jimmy Armas\} and Sanchez, \{David Mauricio\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019 ; Conference date: 02-10-2019 Through 04-10-2019",

year = "2019",

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Morales, EA, Ramos, BM, Aguirre, JA & Sanchez, DM 2019, Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks. in MAR Martinez (ed.), 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings., 8960694, 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019, Bogota, Colombia, 2/10/19. https://doi.org/10.1109/CONIITI48476.2019.8960694

Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks. / Morales, Eduardo Alarcon; Ramos, Brian Mora; Aguirre, Jimmy Armas et al.
2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings. ed. / Monica Andrea Rico Martinez. Institute of Electrical and Electronics Engineers Inc., 2019. 8960694 (2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks

AU - Morales, Eduardo Alarcon

AU - Ramos, Brian Mora

AU - Aguirre, Jimmy Armas

AU - Sanchez, David Mauricio

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

KW - Analysis algorithms

KW - Bayesian networks

KW - Credit Scoring

KW - Data analysis

KW - Microfinance

KW - Moroseness

KW - risk

UR - https://www.scopus.com/pages/publications/85079071172

U2 - 10.1109/CONIITI48476.2019.8960694

DO - 10.1109/CONIITI48476.2019.8960694

M3 - Contribución a la conferencia

AN - SCOPUS:85079071172

T3 - 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings

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

A2 - Martinez, Monica Andrea Rico

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - 5th International Conference on Innovation and Trends in Engineering, CONIITI 2019

Y2 - 2 October 2019 through 4 October 2019

ER -

Morales EA, Ramos BM, Aguirre JA, Sanchez DM. Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks. In Martinez MAR, editor, 2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8960694. (2019 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2019 - Conference Proceedings). doi: 10.1109/CONIITI48476.2019.8960694

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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