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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.
Mr. Peter F. Christoffersen and Mr. Lorenzo Giorgianni
When constructing hedged interest rate arbitrage portfolios for basket currencies, two issues arise: first, how are the unknown future basket weights optimally forecasted from past exchange rate data? And, second, how is risk—in terms of the conditional variance of expected profits from the interest rate arbitrage portfolio—appropriately measured when the basket weights are time-varying? Answers to these questions are provided within a time-varying parameter modeling framework estimated through the Kalman filter. An empirical application is devoted to the experience of the Thai baht currency basket (January 1992–February 1997).
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.
International Monetary Fund. Western Hemisphere Dept.

The economy is still in its deepest recession in decades, partly the result of the failure of past policies. The recession has been aggravated by a political crisis, which had, until recently, paralyzed policymaking and further damaged confidence. President Rousseff was impeached for responsibility crimes related to fiscal practices on August 31, and the government that took office in May will remain in charge until January 1st, 2019. Markets have responded positively to the new government's reform agenda, bolstering asset prices and confidence and helping the country ride a positive wave of sentiment toward emerging economies. However, while some high-frequency indicators suggest the recession may be nearing its end, the implementation of much-needed reforms to durably restore policy credibility is subject to risks.