Credit scoring using machine learning and deep Learning-Based models

Sami Mestiri; Sami Mestiri

doi:10.3934/DSFE.2024009

Data Science in Finance and Economics

2024, Volume 4, Issue 2: 236-248. doi: 10.3934/DSFE.2024009

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Credit scoring using machine learning and deep Learning-Based models

Sami Mestiri ^{1,2
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1.
Faculty of Management and Economic Sciences of Mahdia, University of Monastir, Tunisia
2.
Sidi Messaoud Hiboun, Mahdia, Tunisia

Received: 23 December 2023 Revised: 15 March 2024 Accepted: 10 April 2024 Published: 07 May 2024
JEL Codes: C530, G210, G33

Credit scoring is a useful tool for assessing the capability of customers repayments. The purpose of this paper is to compare the predictive abilities of six credit scoring models: Linear Discriminant Analysis (LDA), Random Forests (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM) and Deep Neural Network (DNN). To compare these models, an empirical study was conducted using a sample of 688 observations and twelve variables. The performance of this model was analyzed using three measures: Accuracy rate, F1 score, and Area Under Curve (AUC). In summary, machine learning techniques exhibited greater accuracy in predicting loan defaults compared to other traditional statistical models.
- credit scoring,
- machine learning,
- artificial intelligence,
- model comparison,
- personal loan
Citation: Sami Mestiri. Credit scoring using machine learning and deep Learning-Based models[J]. Data Science in Finance and Economics, 2024, 4(2): 236-248. doi: 10.3934/DSFE.2024009

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Abstract

Credit scoring is a useful tool for assessing the capability of customers repayments. The purpose of this paper is to compare the predictive abilities of six credit scoring models: Linear Discriminant Analysis (LDA), Random Forests (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM) and Deep Neural Network (DNN). To compare these models, an empirical study was conducted using a sample of 688 observations and twelve variables. The performance of this model was analyzed using three measures: Accuracy rate, F1 score, and Area Under Curve (AUC). In summary, machine learning techniques exhibited greater accuracy in predicting loan defaults compared to other traditional statistical models.

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