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Middle East and Central Asia > Jordan

You are looking at 1 - 2 of 2 items for :

  • Type: Journal Issue x
  • Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill x
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Cover Technical Assistance Reports
Jordan: Technical Assistance Report-Developing Supervisory Review and Evaluation Process SRP Framework
International Monetary Fund. Middle East and Central Asia Dept.
The International Monetary Fund (IMF)’s Middle East Regional Technical Assistance Center (METAC) is currently assisting the Central Bank of Jordan (CBJ) in enhancing its risk-based supervision through the development of a Supervisory Review and Evaluation SRP framework inspired from European Central Bank (ECB) methodology. The Technical Assistance TA mission is part of a multi-step medium-term project. The TA mission aimed to design, in coordination with CBJ, a progressive multi-step roadmap defining the major milestones for a full implementation of SRP. The mission noted that several dimensions should be taken into consideration when implementing the SRP, most notably bridging the data gap by building a fully-fledged supervisory risk database through a dedicated IT project, assessing whether the current organization of the Banking Supervisory Department should be adjusted, and progressively cover all material sources of risks in the SRP.
Keywords:
evaluation process SRP framework; SRP framework; CBJ's supervision staff; SRP approach; IMF's Monetary; Bank supervision; Commercial banks; Risk-based supervision; Credit risk; Global
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