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International Monetary Fund. Monetary and Capital Markets Department
This Basel Core Principles (BCP) for Effective Banking Supervision Detailed Assessment Report has been prepared in the context of the Financial Sector Assessment Program for the People’s Republic of China–Hong Kong Special Administrative Region (HKSAR). The Hong Kong Monetary Authority (HKMA) supervises a major international financial center which was affected, though not significantly so, by the financial crisis. The HKMA is maintaining its commitment to the international regulatory reform agenda and is an early adopter of many standards. Supervisory practices, standards, and approaches are well integrated, risk based and of very high quality. There is one area in relation to the overarching legislative framework and powers which warrants further attention. The HKMA enjoys clear de facto but not de jure operational independence. There are two important cross border dimensions for Hong Kong as an international financial center. One is related to HKSAR’s significant position as a host supervisor. The second is the increasing importance of Mainland China in the current portfolios and prospects of the locally incorporated institutions, and indeed in the choice of HKSAR as a platform for overseas institutions to establish relationships with Mainland China.
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.
Nan Hu, Jian Li, and Alexis Meyer-Cirkel
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.