Credit risk management: a comparative study of ML techniques applied to credit scoring
by Adil Oualid; Abdderrahim Hansali; Lahcen Moumoun
International Journal of Management Practice (IJMP), Vol. 17, No. 5, 2024

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.

Online publication date: Tue, 03-Sep-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Management Practice (IJMP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com