Machine Learning Techniques in Credit Default Prediction
Journal
Advances in Computational Intelligence
Lecture Notes in Computer Science
ISSN
0302-9743
1611-3349
Date Issued
2022
Author(s)
Malagon, Emmanuel
Troncoso, Daniel
Rubio, Andrés
Type
text::book::book part
Abstract
Digital transformation after the pandemic is a must if a company wants to survive in a highly competitive environment. Machine Learning (ML) applications are no strangers to Digital Transformations, and banks are looking for ways to improve efficiency by means of similar technologies. In this work, we propose a machine learning model for predicting the credit default using the LendingClub public dataset. The accepted loans include data ranging from 2007 to 2017. For this purpose, we implement support vector machines and logistic regression models. The results showed that support vector machines is a high accurate model (93%) for predicting the credit default. © 2023 Springer Nature Switzerland AG. Part of Springer Nature.