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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 52 items for :

  • Type: Journal Issue x
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Cover Policy Papers
Poverty Reduction and Growth Trust: 2024 Borrowing Agreements with Belgium, Denmark, France, Japan, Korea, And Qatar
International Monetary Fund. Finance Dept.
and
International Monetary Fund. Legal Dept.
This paper presents the last six borrowing agreements that were concluded between October 2023 and February 2024 to provide new loan resources to the Poverty Reduction and Growth Trust (PRGT) as part of the loan mobilization round launched in July 2021 to support low-income countries (LICs) during the pandemic and beyond. Five of the six agreements use SDRs in the context of SDR channeling. Together these borrowing agreements provide a total amount of SDR 3.9 billion in new PRGT loan resources. The 2021 loan fundraising campaign was concluded successfully. It mobilized total contributions of SDR 14.65 billion from 17 PRGT lenders, well exceeding the SDR 12.6 billion loan target.
Keywords:
transparency policy; PRGT loan disbursement; loan resource; SDR interest rate assumption; loan pledge; Loans; Poverty reduction and development; SDR interest rate
Cover Policy Papers
Resilience and Sustainability Trust―2024 Contribution Agreements with Belgium, Malta, Qatar, and Switzerland
International Monetary Fund. Finance Dept.
and
International Monetary Fund. Legal Dept.
This paper presents Resilience and Sustainability (RST) contribution agreements finalized with four contributors between October 2023 and March 15, 2024. The concluded agreements provide for contributions in a total amount of about SDR 1.2 billion across the three RST accounts – the loan account, deposit account, and reserve account. The new agreements with four members add critical resources that support the continued smooth operations of the RST.
Keywords:
Resilience and Sustainability Trust (RST); Contribution agreements; Resource adequacy; Funding; Contributions; transparency policy; deposit account claim; DA agreement; DA portion; DA contribution; DA assets; Loans; SDR interest rate
Cover FinTech Notes
Generative Artificial Intelligence in Finance: Risk Considerations
Mr. Ghiath Shabsigh
and
El Bachir Boukherouaa
In recent years, technological advances and competitive pressures have fueled rapid adoption of artificial intelligence (AI) in the financial sector, and this adoption is set to accelerate with the recent emergence of generative AI (GenAI). GenAI is a significant leap forward in AI technology that enhances its utility for financial institutions that have been quick at adapting it to a broad range of applications. However, there are risks inherent in the AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. GenAI could aggravate some of these risks and bring about new types or risks as well, including for financial sector stability. This paper provides early insights into GenAI’s inherent risks and their potential impact on the financial sector.
Keywords:
artificial intelligence; embedded bias; cybersecurity; fintech; financial risks; financial sector policy and analysis; financial stability; generative; hallucination; machine learning; privacy; synthetic data; sample generative AI application; AI-machine learning; IMF Library; IMF Fintech Note 2023/006; generative artificial intelligence in finance; Financial sector; Global
Cover IMF Staff Country Reports
Republic of Latvia: Selected Issues
International Monetary Fund. African Dept.
Selected Issues
Keywords:
term premium; premium in the Baltic; staff team of the International Monetary Fund; nominal NPLS; Latvia's lending rate; Loans; Credit; Big data; Nonperforming loans; Bank credit; Baltics
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 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
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
Kenya: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper analyzes Kenya’s success in boosting financial inclusion. Kenya has become a regional and global leader in mobilizing new technologies to advance financial inclusion, poverty reduction, and growth. The rapid progress of financial inclusion in Kenya has been a result of a friendly environment for the absorption of information technology, dynamic local banks, and open and stable regulations. Advances in financial inclusion over the past 10 years have allowed Kenyans to reap many of the benefits of financial access at a much faster pace than the typical cycle of financial deepening in low- and middle-income countries. Mobile financial services have lowered the transaction cost of remittances, allowing Kenyan households to smooth consumption in the face of shocks and significantly reducing poverty.
Keywords:
ISCR; CR; Kenya; interest rate; interest rate control; money; bank; NFCS credit; lending rate; Kenya's NFCS; foreign currency; transaction cost; remittance transaction; Interest rate policy; Double taxation; Financial inclusion; Credit; Bank credit; Africa; Global
Cover IMF Staff Country Reports
Qatar: Selected Issues
International Monetary Fund. Middle East and Central Asia Dept.
This Selected Issues paper analyzes the performance and vulnerabilities of Qatar’s nonfinancial corporate (NFC) sector. Qatar’s NFC sector is sizable in terms of the overall share of economic activity. The total turnover of these companies was US$ 28 billion in 2016. Assets of listed and non-listed NFCs in Qatar were estimated at about 115 percent of non-hydrocarbon GDP in 2016. Although profitability of Qatari corporates, as measured by Return on Equity and Return on Assets, has declined, it is still high. Qatari companies remain resilient in the face of moderate to severe interest and earnings shocks, as median Interest Coverage Ratio of Qatari firms remains well above 1. The impact of these shocks on debt-at-risk and firms-at-risk is also limited.
Keywords:
ISCR; CR; private sector; earnings; credit growth; bank; NFC assets; earnings shock; IMF staff calculation; Qatari NFC sector; equity price; Qatar's Interest Coverage Ratio; Nonperforming loans; Oil prices; Credit; Stress testing; Commercial banks