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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 10 items for :

  • Type: Journal Issue x
  • Financial regulation and supervision x
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Cover IMF Staff Country Reports
Singapore: 2024 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Singapore
International Monetary Fund. Asia and Pacific Dept
The 2024 Article IV Consultation with Singapore highlights that following a slowdown in 2023, growth is projected to recover gradually to 2.1 percent in 2024. After reaching 6.1 percent in 2022, inflation has steadily declined to 2.7 percent in April 2024. The pace of disinflation has nonetheless been gradual, with signs of persistent price pressures including from a tight labor market. With risks to global growth now broadly balanced, downside risks to growth outlook have diminished relative to last year, but Singapore remains vulnerable to a deepening of geo-economic fragmentation. Inflation risks remain tilted to the upside. The broadly neutral fiscal stance relative to 2023 will complement the tight monetary policy stance in achieving price stability, while targeted support to vulnerable households and firms will provide temporary relief from high costs of living and business. Singapore’s financial sector remains resilient with solid capital and liquidity buffers, though vigilance against pockets of vulnerabilities is warranted, including from potential systemic risks arising from the housing market. In this context, the tight macroprudential policy stance remains appropriate.
Keywords:
article IV consultation discussion; FSAP update; Singapore economy; CPI-All Items inflation; disinflation trend; Labor markets; Inflation; Artificial intelligence; Liquidity requirements; Global; Asia and Pacific
Cover Departmental Papers
Assessing Banking Sector Vulnerabilities in the Gulf Cooperation Council in the Wake of COVID-19
Abdullah Al-Hassan
,
Imen Benmohamed
,
Aidyn Bibolov
,
Giovanni Ugazio
, and
Ms. Tian Zhang
The Gulf Cooperation Council region faced a significant economic toll from the COVID-19 pandemic and oil price shocks in 2020. Policymakers responded to the pandemic with decisive and broad measures to support households and businesses and mitigate the long-term impact on the economy. Financial vulnerabilities have been generally contained, reflecting ongoing policy support and the rebound in economic activity and oil prices, as well as banks entering the COVID-19 crisis with strong capital, liquidity, and profitability. The banking systems remained well-capitalized, but profitability and asset quality were adversely affected. Ongoing COVID-19 policy support could also obscure deterioration in asset quality. Policymakers need to continue to strike a balance between supporting recovery and mitigating risks to financial stability, including ensuring that banks’ buffers are adequate to withstand prolonged pandemic and withdrawal of COVID-related policy support measures. Addressing data gaps would help policymakers to further assess vulnerabilities and mitigate sectoral risks.
Keywords:
COVID-19; banking performance; corporates performance; sovereign-banks-economy nexus; bank asset quality; oil price shock; policy support; assessing banking sector vulnerability; C. bank assets quality; Financial statements; Commercial banks; Loans; Credit; Global; Middle East
Cover Staff Discussion Notes
Toward a Global Approach to Data in the Digital Age
Mr. Vikram Haksar
,
Mr. Yan Carriere-Swallow
,
Emran Islam
,
Andrew Giddings
,
Kathleen Kao
,
Emanuel Kopp
, and
Gabriel Quiros
The ongoing economic and financial digitalization is making individual data a key input and source of value for companies across sectors, from bigtechs and pharmaceuticals to manufacturers and financial services providers. Data on human behavior and choices—our “likes,” purchase patterns, locations, social activities, biometrics, and financing choices—are being generated, collected, stored, and processed at an unprecedented scale.
Keywords:
Data, finance, bigtech, competition, privacy, trade, policy coordination, global principles; data portability; DATA policies country; privacy trade-off; policy divergence; data availability divide; data economy; Digital economy; Competition; Financial sector stability; Artificial intelligence; Global
Cover IMF Staff Country Reports
People’s Republic of China–Hong Kong Special Administrative Region: Financial Sector Assessment Program-Technical Note-Banking Sector: Supervision and Regulation
International Monetary Fund. Monetary and Capital Markets Department
Banking supervision and regulation by the Hong Kong Monetary Authority (HKMA) remain strong. This assessment confirms the 2014 Basel Core Principles assessment that the HKMA achieves a high level of compliance with the BCPs. The Basel III framework (and related guidance) and domestic and cross-border cooperation arrangements are firmly in place. The HKMA actively contributes to the development and implementation of relevant international standards. Updating their risk based supervisory approach helped the HKMA optimize supervisory resources. The HKMA’s highly experienced supervisory staff is a key driver to achieving one of the most sophisticated levels of supervision and regulation observed in Asia and beyond.
Keywords:
People's republic of china-Hong kong special administrative region; B. banking sector structure; holding company; China banking; People's Republic of China-Hong Kong Special Administrative Region FSAP; Bank supervision; Artificial intelligence; Commercial banks; Basel Core Principles; Bank soundness; Global; Central Asia; Asia and Pacific; External audit
Cover IMF Working Papers
Fintech Credit Risk Assessment for SMEs: Evidence from China
Yiping Huang
,
Ms. Longmei Zhang
,
Zhenhua Li
,
Han Qiu
,
Tao Sun
, and
Xue Wang
Promoting credit services to small and medium-size enterprises (SMEs) has been a perennial challenge for policy makers globally due to high information costs. Recent fintech developments may be able to mitigate this problem. By leveraging big data or digital footprints on existing platforms, some big technology (BigTech) firms have extended short-term loans to millions of small firms. By analyzing 1.8 million loan transactions of a leading Chinese online bank, this paper compares the fintech approach to assessing credit risk using big data and machine learning models with the bank approach using traditional financial data and scorecard models. The study shows that the fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages. BigTech’s proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow. Furthermore, the fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers. With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.
Keywords:
WP; credit history; Fintech firm; house ownership; internet company; real-time customer rating; Fintech lending; Credit risk assessment; Big data; Machine learning; micro firm; firms' access; medium-size enterprise; firm size; adverse selection; firm's real-time customer rating; fixed cost; information advantage; BigTech company; MYbank loan; Fintech; Credit risk; Loans; Bank credit; b. firm location; fintech approach; cash flows
Cover IMF Working Papers
Completing the Market: Generating Shadow CDS Spreads by Machine Learning
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.
Keywords:
WP; firm; default probability; failure intensity; firm size proxy; Credit default swaps; Prediction; Machine Learning methods; input variable; contracts data; recovery rate; importance probability matrix; credit measure Distance to default; coverage ratio; equity market variable; macroeconomic variable; market perception; machine learning method; Credit default swap; Machine learning; Credit risk; Credit ratings; Stock markets; North America
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 IMF Staff Country Reports
People’s Republic of China-Hong Kong Special Administrative Region: Financial Sector Assessment Program-Basel Core Principles for Effective Banking Supervision-Detailed Assessment of Observance
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.
Keywords:
ISCR; CR; risk management; senior management; holding company; banking group; risk profile; chief executive; incorporated AIs; credit risk; interest rate; financial system; capital base; corporate governance; audit program; Artificial intelligence; Market risk; Credit risk; External audit; Capital adequacy requirements; Global
Cover IMF Working Papers
Nonperforming Loans in the GCC Banking System and their Macroeconomic Effects
Mr. Raphael A Espinoza
and
Mr. Ananthakrishnan Prasad
According to a dynamic panel estimated over 1995 - 2008 on around 80 banks in the GCC region, the NPL ratio worsens as economic growth becomes lower and interest rates and risk aversion increase. Our model implies that the cumulative effect of macroeconomic shocks over a three year horizon is indeed large. Firm-specific factors related to risk-taking and efficiency are also related to future NPLs. The paper finally investigates the feedback effect of increasing NPLs on growth using a VAR model. According to the panel VAR, there could be a strong, albeit short-lived feedback effect from losses in banks’ balance sheets on economic activity, with a semi-elasticity of around 0.4.
Keywords:
WP; bank; interest rate; NPL ratio; loan; GCC country; NPLs; macro-financial linkages; Gulf Cooperation Council; Stress-testing; banking system; GCC economy; bank Muscat SAOG; feedback effect; banks in the Gulf Cooperative Council; GCC countries data; balance sheet constraint; GCC region; Nonperforming loans; Credit; Commercial banks; Credit risk; Loans; Global
Cover IMF Working Papers
Oil Prices and Bank Profitability: Evidence From Major Oil-Exporting Countries in the Middle East and North Africa
Mr. Heiko Hesse
and
Mr. Tigran Poghosyan
This paper analyzes the relationship between oil price shocks and bank profitability. Using data on 145 banks in 11 oil-exporting MENA countries for 1994-2008, we test hypotheses of direct and indirect effects of oil price shocks on bank profitability. Our results indicate that oil price shocks have indirect effect on bank profitability, channeled through country-specific macroeconomic and institutional variables, while the direct effect is insignificant. Investment banks appear to be the most affected ones compared to Islamic and commercial banks. Our findings highlight systemic implications of oil price shocks on bank performance and underscore their importance for macroprudential regulation purposes in MENA countries.
Keywords:
WP; bank; price; oil price shock; profitability; bank profitability; oil price shocks; system GMM; bank inefficiency; bank owner; world oil price; bank specialization; terms of bank assets; bank behavior; macroeconomic factor; bank performance; bank type; shock variable; bank size; cyclical bank lending; Oil prices; Bank soundness; Credit risk; Islamic banking; Inflation; Global; Middle East; North Africa; East Africa