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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.
Ms. Ghada Fayad, Chengyu Huang, Yoko Shibuya, and Peng Zhao

program is on trach or not. Though missions and corresponding reports may be separate, Article IV consultations are often combined with use of fund resources papers (program request and program reviews). 7 Developed by the Knowledge Management Unit at the Fund. 8 Our word2vec model is trained on all externally published IMF documents. We trained the model for 160 iterations with some commonly recommended hyperparameters (300 dimensions and windows size of 5). 9 SVM is a supervised machine learning model for classification tasks (in our setting

Majid Bazarbash

to a new line of business with vastly different risk properties, a supervised learning model could yield misleading predictions and is therefore inappropriate to use, even if the model displays high performance measures on the original dataset. Therefore, expert judgment is needed to ensure the dataset’s relevance to the analysis, for example, by ruling out deep structural changes in the environment. Classification versus regression models Supervised machine learning models are classified based on the type of the outcome variable. The outcome variable is a

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.