FinTech Lending to Borrowers with No Credit History

Laura Chioda, Paul Gertler, Sean Higgins and Paolina C. Medina

No 33208, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms. Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.

JEL-codes: G23 G5 O16 (search for similar items in EconPapers)
Date: 2024-11
New Economics Papers: this item is included in nep-big, nep-fmk, nep-inv and nep-pay
Note: CF DEV
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w33208.pdf (application/pdf)
Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:33208

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w33208
The price is Paper copy available by mail.

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().