Weekly Research Rundown – June 21, 2019

Weekly Research Rundown – June 21, 2019

Climate Adaptation Investment and the Community Reinvestment Act (Keenan and Mattiuzzi)

This report explains how banks can use the CRA to invest in communities in the face of climate change. The authors provide an interpretation of existing administrative authority that allows for climate adaptation investments in disaster areas and a survey of recent investments that may be eligible for CRA consideration. A spatial analysis finds that the majority of counties in which disasters are declared contain census tracts that are already eligible for CRA investment.

Read More: https://www.frbsf.org/community-development/files/climate-adaptation-investment-and-the-community-reinvestment-act.pdf

Consumer-Lending Discrimination in the FinTech Era (Bartlett, Morse, Stanton, and Wallace)

This paper studies discrimination in both face-to-face and algorithmic lending. Using Fannie Mae and Freddie Mac mortgage data, the authors find that lenders charge Latinx and African-American borrowers higher interest rates for mortgage loans. Though FinTech algorithms discriminate as well, they discriminate less than face-to-face lenders.

Read More: https://www.nber.org/papers/w25943

From Market Making to Matchmaking: Does Bank Regulation Harm Market Liquidity? (Saar et al)

This paper studies the effect of post-crisis bank regulations on investor welfare. The authors find that increased regulatory costs can lower average transaction costs in corporate bond markets during normal times, improving overall investor welfare. Bank dealers in over-the-counter markets optimize between market making and matchmaking trading mechanisms: post-crisis regulations increased the cost of market making, incentivizing banks to invest in more efficient matchmaking mechanisms.

Read More: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3399063

Fintech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk (Bazarbash)

This paper offers a discussion of the strengths and weaknesses of machine learning credit assessment, presenting the core ideas and fundamental challenges for a nontechnical audience. The authors explain that machine learning methods have the potential to increase financial inclusion, but special attention must be paid to the relevance of data used for analysis in order to avoid discrimination, agency problems, and the counterfeiting of indicators by borrowers.

Read More: https://www.imf.org/en/Publications/WP/Issues/2019/05/17/FinTech-in-Financial-Inclusion-Machine-Learning-Applications-in-Assessing-Credit-Risk-46883