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A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation.

Authors :
Hasni, Marwa
Aguir, Mohamed Salah
Babai, Mohamed Zied
Jemai, Zied
Source :
International Journal of Supply & Operations Management; May2024, Vol. 11 Issue 2, p168-187, 20p
Publication Year :
2024

Abstract

Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower's characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVMLSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23831359
Volume :
11
Issue :
2
Database :
Complementary Index
Journal :
International Journal of Supply & Operations Management
Publication Type :
Academic Journal
Accession number :
177386507
Full Text :
https://doi.org/10.22034/IJSOM.2023.109898.2722