Credit Risk Analysis Using Machine and Deep Learning Models
Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In...
Main Authors: | Peter Martey Addo, Dominique Guegan, Bertrand Hassani |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-04-01
|
Series: | Risks |
Subjects: | |
Online Access: | http://www.mdpi.com/2227-9091/6/2/38 |
Similar Items
-
Regulatory learning: How to supervise machine learning models? An application to credit scoring
by: Guégan, D., et al.
Published: (2018) -
Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans
by: Bernard Dushimimana, et al.
Published: (2020-08-01) -
The Influence of Financial Inclusion on Credit Risks in Commercial Banks in Indonesia
by: Reza Ghasarma, et al.
Published: (2019-08-01) -
Credit Risk Model Based on Central Bank Credit Registry Data
by: Fisnik Doko, et al.
Published: (2021-03-01) -
Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
by: Agustin Pérez-Martín, et al.
Published: (2020-11-01)