International Monetary Fund. Communications Department
Address at the Bank of England Twentieth Anniversary Conference
September 29, 2017
International Monetary Fund Managing Director Christine Lagarde delivered this address at the Bank of England conference, “Independence—20 Years On” in London, U.K., on September 29, 2017.
International Monetary Fund. Monetary and Capital Markets Department
he Hong Kong Special Administrative Region (HKSAR) is among the world’s major fintech hubs, well positioned to develop fintech initiatives from its traditional strengths in financial services. Key factors enabling the HKSAR to emerge as a fintech hub include its presence as an international financial center, its free-flowing talent and capital, a highly developed information and technology communication (ITC) infrastructure, and its most unique trait, a geographical and strategic advantage by proximity to the market in Mainland China.
Knightian uncertainty (ambiguity) implies presence of uninsurable risks. Institutional quality may be a good indicator of Knightian uncertainty. This paper correlates non-life insurance penetration in 70 countries with income level, financial sector depth, country risk, a measure of cost of insurance, and the World Bank governance indexes. We find that institutional quality-transparency-uncertainty nexus is the dominant determinant of insurability across countries, surpassing the explanatory power of income level. Institutional quality, as it reflects on the level of uncertainty, is the deeper determinant of insurability. Insurability is lower when governance is weaker.
Ms. Ghada Fayad, Chengyu Huang, Yoko Shibuya, and Peng Zhao
This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries’ reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.
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.