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Tamas Gaidosch, Frank Adelmann, Anastasiia Morozova, and Christopher Wilson
This paper highlights the emerging supervisory practices that contribute to effective cybersecurity risk supervision, with an emphasis on how these practices can be adopted by those agencies that are at an early stage of developing a supervisory approach to strengthen cyber resilience. Financial sector supervisory authorities the world over are working to establish and implement a framework for cyber risk supervision. Cyber risk often stems from malicious intent, and a successful cyber attack—unlike most other sources of risk—can shut down a supervised firm immediately and lead to systemwide disruptions and failures. The probability of attack has increased as financial systems have become more reliant on information and communication technologies and as threats have continued to evolve.
Mr. Ghiath Shabsigh, Mr. Tanai Khiaonarong, and Mr. Harry Leinonen
Major transformations in payment and settlements have occurred in generations. The first generation was paper-based. Delivery times for payment instruments took several days domestically and weeks internationally. The second generation involved computerization with batch processing. Links between payment systems were made through manual or file-based interfaces. The change-over period between technologies was long and still some paper-based instruments like checks and cash remain in use. The third generation, which has been emerging, involves electronic and mobile payment schemes that enable integrated, immediate, and end-to-end payment and settlement transfers. For example, real-time gross settlement systems have been available in almost all countries. DLT has been viewed as a potential platform for the next generation of payment systems, enhancing the integration and the reconciliation of settlement accounts and their ledgers. So far, experiments with DLT experimentations point to the potential for financial infrastructures to move towards real-time settlement, flatter structures, continuous operations, and global reach. Testing in large-value payments and securities settlement systems have partly demonstrated the technical feasibility of DLT for this new environment. The projects examined analyzed issues associated with operational capacity, resiliency, liquidity savings, settlement finality, and privacy. DLT-based solutions can also facilitate delivery versus payment of securities, payment versus payment of foreign exchange transactions, and efficient cross-border payments.
Mr. Charles R Taylor, Christopher Wilson, Eija Holttinen, and Anastasiia Morozova
Fintech developments are shaking up mandates within the existing regulatory architecture. It is not uncommon for financial sector agencies to have multiple policy objectives. Most often the policy objectives for these agencies reflect prudential, conduct and financial stability policy objectives. In some cases, financial sector agencies are also allocated responsibility for enhancing competition and innovation. When it comes to fintech, countries differ to some extent in the manner they balance the objectives of promoting the development of fintech and regulating it. Countries see fintech as a means of achieving multiple policy objectives sometimes with lesser or greater degrees of emphasis, such as accelerating development and spurring financial inclusion, while others may support innovation with the objective of promoting competition and efficiency in the provision of financial services. This difference in emphasis may impact institutional structures, including the allocation of staff resources. Conflicts of interest arising from dual roles are sometimes managed through legally established prioritization of objectives or establishment of separate internal reporting lines for supervision and development.
International Monetary Fund. Monetary and Capital Markets Department
The purpose of the missions of Phase I was to develop a functional central bank, including establishing a modern banking supervisory regime. Especially, MCM provided TA missions under the Phase I that have focused on operationalizing banking license capacity, development of on and offsite supervisory capability, and other relevant areas.
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.
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.
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.
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.
Antoine Bouveret
Cyber risk has emerged as a key threat to financial stability, following recent attacks on financial institutions. This paper presents a novel documentation of cyber risk around the world for financial institutions by analyzing the different types of cyber incidents (data breaches, fraud and business disruption) and identifying patterns using a variety of datasets. The other novel contribution that is outlined is a quantitative framework to assess cyber risk for the financial sector. The framework draws on a standard VaR type framework used to assess various types of stability risk and can be easily applied at the individual country level. The framework is applied in this paper to the available cross-country data and yields illustrative aggregated losses for the financial sector in the sample across a variety of scenarios ranging from 10 to 30 percent of net income.