Welfare-centric Credit Scoring in Nigeria
Corresponding PI: Joshua Blumenstock
While financial service providers have traditionally employed loan officers to decide who can borrow and how much given anticipated repayment rates, digital credit products make lending decisions automatically using algorithms. Algorithmic credit scoring lowers the costs of lending and provides an opportunity to inspect and re-optimize lending decisions to achieve objectives beyond repayment. However, the impacts of these digital credit products remain understudied. In Nigeria, the research team works with a financial service provider to rigorously evaluate the impacts of a digital credit product on clients. The evaluation will investigate the impacts of digital loans on financial welfare, ability to cope with shocks, women’s empowerment, and mental health outcomes; and will determine whether these impacts differ by borrower gender.
Fintech Innovation to Promote Financial Access During Pandemics
Corresponding PIs: Ganesh Iyer, Przemyslaw Jeziorski
The research team aims to evaluate the impact of a novel V-KYC product on the expansion of and access to formal banking in India.
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Subsidized Financial Investments, Homeownership, and Upward Mobility in the United States
Corresponding PI: Ulrike Malmendier
Exploiting variation in areas targeted by the 1992 Federal Housing Enterprise Financial Safety and Soundness Act (1992 GSE Act), the research team will rely on Census and administrative data to investigate whether geographically-targeted policies subsidizing financial investments – in this case, support for mortgage financing – can inadvertently “cement” the disadvantaged social strata of targeted populations.
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Behavioral Biases and Fintech Adoption among Merchants in Mexico
Corresponding PI: Sean Higgins
The research team randomly offers businesses that are already payment technology users the opportunity to be charged a lower merchant fee for each payment they receive from customers.
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Gender-Differentiated Digital Credit Algorithms Using Machine Learning
Corresponding PI: Laura Chioda
Exploring partnerships with financial institutions, the research team aims to augment machine learning credit scoring algorithms used to disburse digital credit by gender-differentiating them.
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Data Science-based Targeting of Small Business Loans for COVID-19 Recovery
Corresponding PI: Paul Gertler
Small and medium enterprises (SMEs) represent 90% of businesses and more than 50% of employment worldwide, making SME survival and profitability a major priority for economic recovery from the COVID-19 crisis. During recessions lenders often reduce the flow of credit to businesses given their creditworthiness models struggle to effectively quantify risk and expected returns during a rapidly changing crisis. Furthermore, while a major component of government policy has been SME lending to support firms and their workforce, uncertainty and risk often limit banks’ willingness to lend to SMEs in the worst-affected sectors. The research team investigates the following to help unlock capital for businesses with high growth potential in Latin America.
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Corresponding PI: Catherine Wolfram
Recent technological innovations have enabled new forms of collateralized lending that overcome difficulties for both financial services providers and consumers in many low- and middle-income countries. In particular, pay-as-you-go financing (PAYGO) has emerged as a way to incrementally finance the purchase of smartphones and solar home systems in emerging markets.