Credit Risk Scoring Model Based on the Discriminant Analysis Technique

Guzman Castillo Stefania; Garizabalo Davila Claudia; Alvear Montoya Luis Guillermo; Gatica Gustavo; Rodriguez Heraz Jaiver Dario; Medina Tovar Freddy Alfonso; Andrade Nieves Sheyla Tatiana

doi:10.1016/j.procs.2023.03.127

Credit Risk Scoring Model Based on the Discriminant Analysis Technique

Guzman Castillo Stefania, Garizabalo Davila Claudia, Alvear Montoya Luis Guillermo, Gatica Gustavo, Rodriguez Heraz Jaiver Dario, Medina Tovar Freddy Alfonso, Andrade Nieves Sheyla Tatiana

Engineering School

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique.

Original language	English
Pages (from-to)	928-933
Number of pages	6
Journal	Procedia Computer Science
Volume	220
DOIs	https://doi.org/10.1016/j.procs.2023.03.127
Publication status	Published - 2023
Event	14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium Duration: 15 Mar 2023 → 17 Mar 2023

Keywords

Cost-effectiveness
credit risk
disbursement
discriminant analysis
financial entities

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1016/j.procs.2023.03.127

Cite this

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title = "Credit Risk Scoring Model Based on the Discriminant Analysis Technique",

abstract = "Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique.",

keywords = "Cost-effectiveness, credit risk, disbursement, discriminant analysis, financial entities",

author = "Stefania, {Guzman Castillo} and Claudia, {Garizabalo Davila} and Guillermo, {Alvear Montoya Luis} and Gatica Gustavo and Dario, {Rodriguez Heraz Jaiver} and Alfonso, {Medina Tovar Freddy} and Tatiana, {Andrade Nieves Sheyla}",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.. All rights reserved.; 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference date: 15-03-2023 Through 17-03-2023",

year = "2023",

doi = "10.1016/j.procs.2023.03.127",

language = "English",

volume = "220",

pages = "928--933",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Credit Risk Scoring Model Based on the Discriminant Analysis Technique

AU - Stefania, Guzman Castillo

AU - Claudia, Garizabalo Davila

AU - Guillermo, Alvear Montoya Luis

AU - Gustavo, Gatica

AU - Dario, Rodriguez Heraz Jaiver

AU - Alfonso, Medina Tovar Freddy

AU - Tatiana, Andrade Nieves Sheyla

PY - 2023

Y1 - 2023

N2 - Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique.

AB - Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique.

KW - Cost-effectiveness

KW - credit risk

KW - disbursement

KW - discriminant analysis

KW - financial entities

UR - http://www.scopus.com/inward/record.url?scp=85164468231&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2023.03.127

DO - 10.1016/j.procs.2023.03.127

M3 - Conference article

AN - SCOPUS:85164468231

SN - 1877-0509

VL - 220

SP - 928

EP - 933

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

Y2 - 15 March 2023 through 17 March 2023

ER -

Credit Risk Scoring Model Based on the Discriminant Analysis Technique

Abstract

Keywords

ASJC Scopus subject areas

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