Abstract
Financial inclusion is a social need that is gaining more and more strength in developing countries. Microcredit is an effective way to enable financial inclusion, but it represents a challenge in portfolio management. This work applies data analytics and machine learning techniques to predict the behavior of the loan default in a non-financial entity. Decision trees have shown the best prediction performance to determine whether a loan will be paid or become irrecoverable after running five predictive models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Banca de las Oportunidades, Superintendencia Financiera de Colombia (2018) Reporte de Inclusión Financiera 2018. https://bancadelasoportunidades.gov.co/sites/default/files/2019-06/RIFFINAL.pdf
Banca de las Oportunidades, Superintendencia Financiera de Colombia (2017) Reporte de Inclusión Financiera 2017. https://bancadelasoportunidades.gov.co/sites/default/files/2018-07/RIF2017LIBROFINAL_WEB02_1.pdf
Bitvai Z, Cohn T (2015) Predicting peer-to-peer loan rates using Bayesian non-linear regression. Proc Natl Conf Artif Intell 3:2203–2209
Byanjankar A (2018) Predicting credit risk in Peer-to-Peer lending with survival analysis. 2017 IEEE Symp Ser Comput Intell SSCI 2017 - Proc 2018-Janua:1–8. https://doi.org/10.1109/SSCI.2017.8280927
Byanjankar A, Heikkila M, Mezei J (2015) Predicting credit risk in peer-to-peer lending: A neural network approach. Proc - 2015 IEEE Symp Ser Comput Intell SSCI 2015 719–725. https://doi.org/10.1109/SSCI.2015.109
Castaño L, Ayala C (2016) Diagnóstico de la Gestión de Cartera en una Empresa Proveedora del Sector Salud en Colombia
CNN Periodismo Digital (2016) Ricard Bonastre: Cómo Generar más ingresos por campaña con algoritmos predictivos. https://www.callcenternews.com.ar/entrevistas/320-cgmi. Accessed 3 Jun 2019
Daza Sandoval LC (2015) Estrategias basadas en el modelo de análisis predictivo árbol de decisión para la mejora del proceso de recaudo de cartera de la línea de vehículo particular del banco Davivienda S.A. Pontificia Universidad Javeriana
Deloitte (2012) Tendencias de cobranza y recuperación de cartera en el sector financiero a partir de la crisis. https://www2.deloitte.com/content/dam/Deloitte/pa/Documents/financial-services/2015-01-Pa-FinancialServices-CobranzaCartera.pdf. Accessed 3 Jun 2019
Do HX, Rösch D, Scheule H (2018) Predicting loss severities for residential mortgage loans: a three-step selection approach. Eur J Oper Res 270:246–259. https://doi.org/10.1016/j.ejor.2018.02.057
Experian (2017) Move debt collection practices into the digital age eResolve. https://www.experian.com/consumer-information/virtual-debt-resolution-negotiation-eResolve.html
Gahlaut A, Tushar, Singh PK (2017) Prediction analysis of risky credit using data mining classification models. 8th internation conference computer communication networks technology ICCCNT 2017. https://doi.org/10.1109/ICCCNT.2017.8203982
Ge R, Feng J, Gu B, Zhang P (2017) Predicting and deterring default with social media information in peer-to-peer lending. J Manag Inf Syst 34:401–424. https://doi.org/10.1080/07421222.2017.1334472
Infórmese (2018) Gestión Analítica de Crédito y Cobranza. https://www.informese.co/gestion-analitica-credito-cobranza/. Accessed 3 Jun 2019
Jiang C, Wang Z, Wang R, Ding Y (2018) Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Ann Oper Res 266:511–529. https://doi.org/10.1007/s10479-017-2668-z
Kim A, Cho SB (2019) An ensemble semi-supervised learning method for predicting defaults in social lending. Eng Appl Artif Intell 81:193–199. https://doi.org/10.1016/j.engappai.2019.02.014
León Sánchez DP (2015) Modelo predictivo para riesgo de liquidez de una entidad fiduciaria usando minería de datos. Universidad Nacional de Colombia
Morgan J, Morgan Grenfell D (1997) Introduction to CreditMetrics. New York
Superintendencia Financiera de Colombia (2019) Evolución cartera de créditos. https://www.superfinanciera.gov.co/inicio/evolucion-cartera-de-creditos-60950. Accessed 3 Jun 2019
Superintendencia Financiera de Colombia (2001) Cartera de Crédito. https://www.superfinanciera.gov.co/publicacion/18575. Accessed 15 Mar 2020
Velásquez AB (2013) Diseño de un modelo predictivo de seguimiento de riesgo de crédito para la cartera comercial, para una entidad financiera del Valle de Aburrá
Zhou J, Li W, Wang J et al (2019) Default prediction in P2P lending from high-dimensional data based on machine learning. Phys a Stat Mech Its Appl 534:122370. https://doi.org/10.1016/j.physa.2019.122370
Zhu L, Qiu D, Ergu D et al (2019) A study on predicting loan default based on the random forest algorithm. Procedia Comput Sci 162:503–513. https://doi.org/10.1016/j.procs.2019.12.017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Herrera Román, J.S., Branch, J.W., Arango-Serna, M.D. (2021). Data Analytics in Financial Portfolio Recovery Management. In: Zapata-Cortes, J.A., Alor-Hernández, G., Sánchez-Ramírez, C., García-Alcaraz, J.L. (eds) New Perspectives on Enterprise Decision-Making Applying Artificial Intelligence Techniques. Studies in Computational Intelligence, vol 966. Springer, Cham. https://doi.org/10.1007/978-3-030-71115-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-71115-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71114-6
Online ISBN: 978-3-030-71115-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)