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A Machine Learning Approach to Credit Allocation

Vamossy, Domonkos F (2020) A Machine Learning Approach to Credit Allocation. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

This dissertation seeks to understand the shortcomings of contemporaneous credit allocation, with a specific focus on exploring how an improved statistical technology impacts the credit access of societally important groups. First, this dissertation investigates a variety of
limitations of conventional credit scoring models, specifically their tendency to misclassify
borrowers by default risk, especially for relatively risky, young, and low income borrowers. Second, this dissertation shows that an improved statistical technology need not to lead to worse outcomes for disadvantaged groups. In fact, the credit access for borrowers belonging to such groups can be improved, while providing more accurate credit risk assessment. Last, this dissertation documents modern-day disparities in debt collection judgments across white and black neighborhoods. Taken together, this dissertation provides valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders and across societally important groups, as well as macroprudential regulation.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Vamossy, Domonkos Fvamossyd@gmail.comdfv5
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairAlbanesi, Stefaniastefania.albanesi@gmail.com
Committee MemberHanley, Douglasdoughanley@pitt.edu
Committee MemberBerkowitz, Danieldmberk@pitt.edu
Committee MemberLinardi, Seralinardi@pitt.edu
Committee MemberLee, Dokyundokyun@cmu.edu
Date: 16 September 2020
Date Type: Publication
Defense Date: 20 July 2020
Approval Date: 16 September 2020
Submission Date: 24 July 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 193
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Economics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Deep Learning, Credit Allocation.
Date Deposited: 16 Sep 2020 15:15
Last Modified: 16 Sep 2020 15:15
URI: http://d-scholarship.pitt.edu/id/eprint/39438

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