BUSI 606 Week 6 DB Forum 3: Part C Example

Discussion Board Forum 3: Part C, Replies

BUSI 606 Global Financial Markets, Discussion Board Forum 3: Part C (replies)

Thread by [Student Name]

The two classmates and their key topics are composites; the research cited is real, and the requirements follow Liberty's public course guide for BUSI 606.

Reply to Kendra

Kendra, your thread argued that online lenders expand credit to people and businesses that banks overlook, and the evidence from 2020 supports you. Erel and Liebersohn (2022) studied the Paycheck Protection Program and found that fintech lenders provided a disproportionate share of loans in areas with fewer bank branches, lower incomes and larger minority populations, and to businesses that were less likely to have an existing bank relationship. When banks favored their established customers, fintech platforms filled part of the gap, which is exactly the role you described.

The evidence on outcomes is less encouraging in consumer lending. Di Maggio and Yao (2021) followed borrowers who took personal loans from fintech platforms, often to consolidate credit card debt. Initially these borrowers had good credit, but within a year many had run their card balances back up, and they were more likely to default than similar borrowers who took loans from traditional lenders. The fintech loan improved their credit scores in the short run while increasing their total debt.

Expanding access to credit is only a benefit if borrowers end up better off. The two studies suggest that fintech lending works well where speed and reach are the main obstacles, as in emergency small business lending, and less well where the product makes it easy to borrow more than a household can manage. Do you think regulators should treat fintech consumer loans differently from fintech small business loans, given these different results? One option would be affordability checks for consumer loans used to refinance card debt, which would not slow emergency business lending.

Reply to Andrew

Andrew, your thread argued that algorithmic lending removes bias because a computer cannot see a borrower's race. There is real evidence for part of that claim. Bartlett et al. (2022), studying mortgages backed by government-sponsored enterprises, found that algorithmic lenders did not discriminate against Latino and Black applicants in deciding whether to approve loans. However, they also found that Latino and Black borrowers paid higher interest rates than otherwise similar White borrowers, and that this pricing difference appeared among fintech lenders as well as traditional ones, though it was smaller. An algorithm can learn to charge more to borrowers who are less likely to shop around, and those borrowers may be concentrated in minority communities.

Fuster et al. (2022) identified a second channel. Comparing traditional credit models with more flexible machine learning models, they found that the newer models predicted default better overall but changed who benefited: White and Asian borrowers were more likely to gain from the change, while Black and Hispanic borrowers were more likely to see higher predicted risk and rates. The flexibility that makes the models accurate also lets them use combinations of variables that track group membership.

These findings do not mean algorithms are worse than loan officers, but they suggest that removing race from the data does not guarantee fair outcomes. Lenders could test their models for differences in approval and pricing across groups before deployment, as fair lending law already expects. Would you support requiring that kind of testing for all algorithmic lenders?

References

Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30-56. https://doi.org/10.1016/j.jfineco.2021.05.047

Di Maggio, M., & Yao, V. (2021). Fintech borrowers: Lax screening or cream-skimming? The Review of Financial Studies, 34(10), 4565-4618. https://doi.org/10.1093/rfs/hhaa142

Erel, I., & Liebersohn, J. (2022). Can FinTech reduce disparities in access to finance? Evidence from the Paycheck Protection Program. Journal of Financial Economics, 146(1), 90-118. https://doi.org/10.1016/j.jfineco.2022.05.004

Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5-47. https://doi.org/10.1111/jofi.13090