Título: Financial Credit Risk Measurement Using a Binary Classification Model

Autor(es): GUEVARA VEGA CATHY PAMELA, LANDETA LOPEZ PABLO ANDRES, QUIÑA MERA JOSE ANTONIO, JAVIER MONTALUISA, OSCAR CHILUIZA

Fecha de publicación: 21-may-2023

Resumen: Currently, financial institutions have several problems in the analysis of information to grant a credit or a loan, causing losses that involve collection expenses, notifications, legal processes, among others. Thanks to the digital transformation and technological progress, Artificial Intelligence and especially Machine Learning can be used to analyze customer data and predict non-compliance with the payment of their obligations to institutions. The objective of this work is to apply CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the Random Forest (XGBoost), Logistic Regression and Neural Networks of supervised learning models, to implement a predictive model that allows evaluating credit risk. With the result of the application of the predictive model, it is concluded that the use of Machine Learning tools helps to optimize the evaluation of credit risk in financial entities. Once CRISP-DM methodology was used for the analysis, development, and evaluation of the models, it was concluded that the most efficient model is the Random Forest. Based on this experience, future work could implement this type of model in other areas such as: fraud detection, customer segmentation or a recommendation engine that can suggest financial products and services based on customer needs and behaviors.

Palabras clave: Predictive model CRISP-DM Random Forest Logistic Regression Neuronal Networks

DOI: https://doi.org/10.1007/978-3-031-32213-6_18

ISSN: 18650937

Tipo publicación: Artículo