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

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)928-933
Número de páginas6
PublicaciónProcedia Computer Science
Volumen220
DOI
EstadoPublicada - 2023
Evento14th 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, Bélgica
Duración: 15 mar. 202317 mar. 2023

Áreas temáticas de ASJC Scopus

  • Ciencia de la Computación General

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