Search Results

You are looking at 1 - 8 of 8 items for :

Clear All
International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
International Monetary Fund. Strategy, Policy, & Review Department

leverages machine-learning (ML) algorithms . Macroeconomic risk assessment is a challenging task: crises are infrequent and almost always involve some elements of surprise. They tend to feature interactions between different parts of the economy and non-linear relationships that are not well measured in “normal times.” ML tools can often better capture these relationships. They can also be more robust to outliers, noise, and the diversity of experiences across countries. The performance of machine-learning-based models is evaluated against more conventional models in a

International Monetary Fund. Strategy, Policy, & Review Department

crisis definitions and assessed within a single model when effective to improve evenhandedness. They also represent a step forward in risk assessment modeling, leveraging the recent advances in modeling macroeconomic risks with ML tools. Machine-learning tools are well-suited to the challenges of macroeconomic risk assessments . Crises are rare and almost always involve novel features (otherwise they would have been anticipated). When they materialize, they often reveal economic relationships which are not regularly observed in normal times, making it challenging to

Majid Bazarbash

) , and Mullainathan & Spiess (2017) . This paper takes a less technical approach and discusses ML in the context of credit risk assessment, which is not the main focus of prior studies. There has been a surge in recent years in the use of ML tools for estimating credit risk, especially since the establishment of Basel II, which called for development of internal credit rating models by banks, and since the global financial crisis. However, internal rating models based on the standard linear econometric approach have been generally shown to exhibit poor performance

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.
Chris Redl and Sandile Hlatshwayo
We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial, socioeconomic, development and political variables. The prediction model correctly forecasts unrest in the following year approximately two-thirds of the time. Shapley values indicate that the key drivers of the predictions include high levels of unrest, food price inflation and mobile phone penetration, which accord with previous findings in the literature.
Chris Redl and Sandile Hlatshwayo

). The 2010 structural-demographic forecast for the 2010 to 2020 decade: A retrospective assessment . PLoS ONE 15 ( 8 ). 7 Appendix I: Machine learning models A range of ML tools were compared for prediction performance. All models were compared using the expanding window approach described in the text, based on the AUC. We use the Python library of Pedregosa et al. (2011) , scikit-learn.org, to implement these algorithms. This section draws heavily on the latter source, as well as Hastie et al. (2001) . 7.1 Linear models: regularized logistic

Mr. Tadatsugu Matsudaira

administrations have been considering a central image analysis center so that all scanned images from border sites are centralized and experienced image analysts are pooled together. For this purpose, a unified file format (UFF) for X-ray images has been developed and adopted by all major scanner equipment manufacturers. The UFF aids the buildup of a nationwide centralized scanned image database where ML tools can run through images in sufficient quantities (usually millions) to build fairly accurate ATD algorithms. While images of non-threat cargoes are abundant, images of