FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk / Majid Bazarbash.

Format:

Book

Government document

Author/Creator:

Bazarbash, Majid.

Series:

IMF Working Papers; Working Paper ; No. 2019/109

IMF Working Papers

Language:

English

Subjects (All):

Finance.

Physical Description:

1 online resource (35 pages)

Place of Publication:

Washington, D.C. : International Monetary Fund, 2019.

Summary:

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.

Notes:

Description based on print version record.

ISBN:

9781498316033

1498316034

9781498316057

1498316050