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Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers

Title	Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers
Author	Öner, T., Alnahas, D., Kanturvardar, A., Ülkgün, A. M., Demiroǧlu, Cenk
Publication Date:	2023
Publication Place	- IEEE
Subject	Class imbalance, Credit risk assessment, Gradient boosting, Machine learning, Neural networks
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-85173461371
Record ID	da957e70-4dfe-442a-a332-c9e2948f9faf
Library Location	Electrical & Electronics Engineering
Date	2023
Sample Text	Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light Gradient-Boosting Machine and Neural Networks are utilized for credit risk assessment. These machine learning models are trained and tested using The Home Credit Default Risk dataset that was obtained from a competition on the website kaggle.com. Resampling techniques were also implemented to tackle the class imbalance problem in the dataset. Moreover, various preprocessing techniques were also utilized to deal with missing values and outliers in the dataset. The study presents the results of experiments with different parameters and preprocessing techniques and showcases the optimal configuration for the best results. The performance metrics of the machine learning models that are implemented in the experiments are compared to the performance metrics of a baseline system that used the Light Gradient-Boosting Machine model without applying preprocessing techniques.
DOI	10.1109/SIU59756.2023.10224008

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Özyeğin University

Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers

Author Öner, T., Alnahas, D., Kanturvardar, A., Ülkgün, A. M., Demiroǧlu, Cenk

Publication Date 2023

Publication Place - IEEE

Subject Class imbalance, Credit risk assessment, Gradient boosting, Machine learning, Neural networks

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-85173461371

Record ID da957e70-4dfe-442a-a332-c9e2948f9faf

Library Location Electrical & Electronics Engineering

Date 2023

Sample Text Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light Gradient-Boosting Machine and Neural Networks are utilized for credit risk assessment. These machine learning models are trained and tested using The Home Credit Default Risk dataset that was obtained from a competition on the website kaggle.com. Resampling techniques were also implemented to tackle the class imbalance problem in the dataset. Moreover, various preprocessing techniques were also utilized to deal with missing values and outliers in the dataset. The study presents the results of experiments with different parameters and preprocessing techniques and showcases the optimal configuration for the best results. The performance metrics of the machine learning models that are implemented in the experiments are compared to the performance metrics of a baseline system that used the Light Gradient-Boosting Machine model without applying preprocessing techniques.

DOI 10.1109/SIU59756.2023.10224008