Renouf Publishing Co. Ltd.
embedded image
Renouf
Online Bookstore

ABOUT SSL CERTIFICATES

 
Quick Search
for: 
in 
 
Advanced search
F.A.Q.
Featured books
New in print
Best Sellers
President's picks

Shopping cart/Checkout  [0]
Sign-up for eUpdates
FinTech in Financial Inclusion
Machine Learning Applications in Assessing Credit Risk

by Majid Bazarbash

Series:Working Paper No. 19/109
ISBN 9781498314428
Code: #WPIEA2019109

Publication year: 2019

Cdn: $27.00; US: $25.00
Paperback
Language: English
34 pages
Add to cart
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
FinTech in Financial Inclusion
Cdn: $27.00; US: $25.00
International Monetary Fund (IMF) BookID: 125113 Added: 2019.6.14