Title: Credit risk management: a comparative study of ML techniques applied to credit scoring

Authors: Adil Oualid; Abdderrahim Hansali; Lahcen Moumoun

Addresses: MISI Laboratory, FSTS, Hassan 1st University of Settat, Morocco ' National School of Business and Management, Cadi Ayyad University Marrakech, Morocco ' MISI Laboratory, FSTS, Hassan 1st University of Settat, Morocco

Abstract: Banks are concerned with controlling and managing credit risk - particularly the risk prudently required by central banks. Consequently, banks are constantly developing models to consider, analyse and predict risk. Credit risk assessment and prediction come in the form of models that calculate scores regarding a business' potential vulnerability. This is done using financial data and a method of calculation. The objective of our work is to study the various AI techniques of credit scoring, and their interests as a powerful tool to predict the creditworthiness of borrowers.

Keywords: credit risk; credit scoring; machine learning; supervised ML; unsupervised ML.

DOI: 10.1504/IJMP.2024.140863

International Journal of Management Practice, 2024 Vol.17 No.5, pp.509 - 521

Received: 20 Dec 2022
Accepted: 09 May 2023
Published online: 03 Sep 2024 *

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