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Middle East and Central Asia > Jordan

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Cover IMF Working Papers
FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk
Majid Bazarbash
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.
Keywords:
WP; ML model; bears risk; machine learning technique; ML analysis; ML evaluation; FinTech Credit; Financial Inclusion; Machine Learning; Credit Risk Assessment; ML analyst; credit risk driver; FinTech credit company; credit scoring; supervised machine learning model; capital structure; borrower default; neural network; Credit risk; Credit; Credit ratings; Loans; Global
Cover The IMF's Data Dissemination Initiative After Ten Years
The IMF's Data Dissemination Initiative After Ten Years
Mr. William E. Alexander
,
Mr. John Cady
, and
Mr. Jesus R Gonzalez-Garcia

Abstract

The Data Dissemination Initiative was launched in the mid-1990s as part of a broader internationally-agreed-upon initiative to strengthen transparency and promote good governance practices by establishing standards and codes. Ten years later, the initiative is viewed as an integral part of the international financial architecture, and is considered to have improved the functioning of international financial markets and contributed to global financial stability. This volume reviews certain aspects of the development of and experience with the initiative over the past decade, and concludes by reflecting on potential challenges ahead and possible enhancements.

Keywords:
BOOK; market efficiency benefit; reserves template; SDDS subscription; market participant; GDDS participant; Special Data Dissemination Standard (SDDS); Data dissemination; International reserves; Reserve assets; Middle East and Central Asia; Global; Asia and Pacific; Western Hemisphere; Africa