FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk

May 17, 2019

Preview Citation

Format: Chicago

Majid Bazarbash. FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk, (USA: International Monetary Fund, 2019) accessed November 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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.

Subject: Credit, Credit ratings, Credit risk, Financial institutions, Financial regulation and supervision, Loans, Machine learning, Money, Technology

Keywords: Bears risk, Borrower default, Capital structure, Credit, Credit ratings, Credit risk, Credit Risk Assessment, Credit risk driver, Credit scoring, Financial Inclusion, FinTech Credit, FinTech credit company, Global, Loans, Machine Learning, Machine learning technique, ML analysis, ML analyst, ML evaluation, ML model, Neural network, Supervised machine learning model, WP

Publication Details

Pages:

34
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2019/109
Stock No:

WPIEA2019109
ISBN:

9781498314428
ISSN:

1018-5941

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