International Monetary Fund. Strategy, Policy, & Review Department
This paper presents traction as a multidimensional concept and discusses a comprehensive and complementary set of approaches to attempt to measure it based on the Fund’s value added to policy dialogue and formulation and public debate in 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.
Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of the “nowcasting” challenge familiar to many central banks. Addressing this problem—and mindful of the pitfalls of extracting information from a large number of correlated proxies—we explore some recent techniques from the machine learning literature. We focus on two popular techniques (Elastic Net regression and Random Forests) and provide an estimation procedure that is intuitively familiar and well suited to the challenging features of Lebanon’s data.
This paper focuses on official intervention on the forward exchange market. The purpose is to provide a straightforward account of the theory of intervention and to use it to discuss the problems raised. The forward exchange market may be conveniently treated in terms of stocks rather than of flows; that is, the forward exchange rate is taken as reconciling the desires of market participants with respect to the holding—rather than the changing—of forward exchange positions. Official intervention in the forward exchange market can be analysed by regarding the authorities either as part of the market or as distinct from it. Official swap transactions are frequently undertaken not on the open market but by direct arrangement with foreign monetary authorities or with commercial banks. The substantial rise to be expected in the forward premium would, of course, have an adverse effect on the foreign balance, which might be unwelcome from a cyclical standpoint though it would probably merely involve a diminution in the improvement that would otherwise have occurred as a result of the recession.