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  • Technological Change: Choices and Consequences; Diffusion Processes x
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Raquel Fernández, Asel Isakova, Francesco Luna, and Barbara Rambousek
This paper considers various dimensions and sources of gender inequality and presents policies and best practices to address these. With women accounting for fifty percent of the global population, inclusive growth can only be achieved if it promotes gender equality. Despite recent progress, gender gaps remain across all stages of life, including before birth, and negatively impact health, education, and economic outcomes for women. The roadmap to gender equality has to rely on legal framework reforms, policies to promote equal access, and efforts to tackle entrenched social norms. These need to be set in the context of arising new trends such as digitalization, climate change, as well as shocks such as pandemics.
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.