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A Novel Survival Analysis-Based Approach for Predicting Behavioral Probability of Default

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Pattern Recognition (MCPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13264))

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Abstract

Obtaining an accurate model for predicting the probability of default is a critical requirement for financial institutions. Nowadays, COVID19 has produced high economic instability bringing borrowers’ delinquencies as well as moratorium regulations. During the KYC process for applicants, it is essential to estimate the probability of default for avoiding write-offs. However, there are several borrowers who, due to the pandemic, could have lost their jobs or decreased their income, producing several borrowers’ delinquencies, even write-offs by frequent delinquencies. Consequently, having a behavioral model for estimating the probability of default during the loan lifetime is vital for financial institutions. Hence, in this paper, we propose the first survival analysis-based approach for predicting the behavioral probability of default. We collected two real financial databases from different countries with different borrowers’ characteristics. From our experimental results, we can conclude that Logistic Hazard provides better results than Deep Hit for predicting the behavioral probability of default. Based on our experimentation and the risk analysis experts, Deep Hit provides inconsistent results for forecasts greater than six months while considering the financial changes due to the COVID19. Otherwise, Logistic Hazard is more accurate in forecasting the behavioral probability of default for a year, and it shows results more appropriate to risk analysis experts.

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Authors and Affiliations

  1. Altair Management Consultants, Calle de José Ortega y Gasset, 22-24, 5th floor, 28006, Madrid, Spain

    Cuauhtémoc Daniel Suárez-Ramírez, Juan-Carlos Martínez & Octavio Loyola-González

Authors
  1. Cuauhtémoc Daniel Suárez-Ramírez
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  2. Juan-Carlos Martínez
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  3. Octavio Loyola-González
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Corresponding author

Correspondence to Octavio Loyola-González .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Osslan Osiris Vergara-Villegas

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Vianey Guadalupe Cruz-Sánchez

  3. Instituto Politécnico Nacional, Mexico City, Mexico

    Juan Humberto Sossa-Azuela

  4. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    Jesús Ariel Carrasco-Ochoa

  5. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    José Francisco Martínez-Trinidad

  6. Autonomous University of Puebla, Puebla, Mexico

    José Arturo Olvera-López

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Suárez-Ramírez, C.D., Martínez, JC., Loyola-González, O. (2022). A Novel Survival Analysis-Based Approach for Predicting Behavioral Probability of Default. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-07750-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07749-4

  • Online ISBN: 978-3-031-07750-0

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