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

Name: 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

İsim	Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers
Yazar	Öner, T., Alnahas, D., Kanturvardar, A., Ülkgün, A. M., Demiroǧlu, Cenk
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Class imbalance, Credit risk assessment, Gradient boosting, Machine learning, Neural networks
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-85173461371
Kayıt Numarası	da957e70-4dfe-442a-a332-c9e2948f9faf
Lokasyon	Electrical & Electronics Engineering
Tarih	2023
Örnek Metin	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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Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers

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

Basım Tarihi 2023

Basım Yeri - IEEE

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

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-85173461371

Kayıt Numarası da957e70-4dfe-442a-a332-c9e2948f9faf

Lokasyon Electrical & Electronics Engineering

Tarih 2023

Örnek Metin 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