Authors: Michael McCanless*, University of Kentucky
Topics: Economic Geography, Cyberinfrastructure
Keywords: Platform, Finance, Alternative Data, Credit, Debt
Session Type: Paper
Presentation File: No File Uploaded
This presentation examines the FinTech industry in the context of a recent explosion in personal loans within U.S. consumer finance. Personal loans, unlike mortgages or auto loans, are not secured against the value of an existing asset, but rather against the future indebtedness of the consumer. FinTech lenders have been at the forefront of this movement, and are increasingly utilizing platform interfaces to incorporate Artificial Intelligence (AI) and Machine Learning (ML) in the extension of lending beyond the parameters of FICO credit scoring. These ‘Alternative’ credit scores can be read as a form of credit scoring that incorporates demographic (i.e. ‘alternative’) data points in determining who is and is not creditworthy. While FICO credit scoring (read: standard credit scoring) has been critiqued for “reflecting” existing racial and gender inequities, this research tests FinTech lending algorithms in order to make the argument that ‘alternative’ credit scoring moves beyond a ‘reflection’ of inequity and renders profiles capable of actively incorporating difference within lending. As industry leaders project a vision that AI and ML will become the cornerstone of consumer finance, the vision projected by FinTech is read as reflective of a broader utopian ideal, in which credit is radically reformulated via AI and ML in order to securitize a platform future for debt.