IDEAS home Printed from https://ideas.repec.org/p/zbw/safewp/369.html
   My bibliography  Save this paper

Consumer credit in the age of AI: Beyond anti-discrimination law

Author

Listed:
  • Langenbucher, Katja

Abstract

Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of antidiscrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine.0F 1 The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.

Suggested Citation

  • Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," SAFE Working Paper Series 369, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:369
    DOI: 10.2139/ssrn.4298261
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/266692/1/1827025034.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.2139/ssrn.4298261?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021. "Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2799-2832.
    2. Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    3. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    4. Christine A. Parlour & Uday Rajan, 2001. "Competition in Loan Contracts," American Economic Review, American Economic Association, vol. 91(5), pages 1311-1328, December.
    5. Heidi Ledford, 2019. "Millions of black people affected by racial bias in health-care algorithms," Nature, Nature, vol. 574(7780), pages 608-609, October.
    6. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
    7. Bartlett, Robert & Morse, Adair & Stanton, Richard & Wallace, Nancy, 2022. "Consumer-lending discrimination in the FinTech Era," Journal of Financial Economics, Elsevier, vol. 143(1), pages 30-56.
    8. Blattner, Laura & Nelson, Scott, 2021. "How Costly Is Noise? Data and Disparities in Consumer Credit," Research Papers 3978, Stanford University, Graduate School of Business.
    9. Marco Di Maggio & Dimuthu Ratnadiwakara & Don Carmichael, 2022. "Invisible Primes: Fintech Lending with Alternative Data," NBER Working Papers 29840, National Bureau of Economic Research, Inc.
    10. Brito, Dagobert L & Hartley, Peter R, 1995. "Consumer Rationality and Credit Cards," Journal of Political Economy, University of Chicago Press, vol. 103(2), pages 400-433, April.
    11. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    12. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
    13. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," LawFin Working Paper Series 42, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," LawFin Working Paper Series 42, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    2. Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    3. Hagendorff, Jens & Nguyen, Duc Duy & Sila, Vathunyoo, 2022. "Does marriage equality promote credit access? Evidence from same-sex marriage laws," Journal of Corporate Finance, Elsevier, vol. 77(C).
    4. Cusato, Antonio & Castillo, José Luis & IDB Invest, 2023. "Access to Credit and the Expansion of Broadband Internet in Peru," IDB Publications (Working Papers) 12922, Inter-American Development Bank.
    5. Tetyana Balyuk, 2023. "FinTech Lending and Bank Credit Access for Consumers," Management Science, INFORMS, vol. 69(1), pages 555-575, January.
    6. Olivier Armantier & Sebastian Doerr & Jon Frost & Andreas Fuster & Kelly Shue, 2024. "Nothing to hide? Gender and age differences in the willingness to share data," BIS Working Papers 1187, Bank for International Settlements.
    7. D Dulani Jayasuriya & Mohamed Ayaz & Michael Williams, 2023. "The use of digital footprints in the US mortgage market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 353-401, March.
    8. Hurtado, Agustin & Sakong, Jung, 2022. "The effect of minority bank ownership on minority credit," Working Papers 325, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    9. Sabrina T. Howell & Theresa Kuchler & David Snitkof & Johannes Stroebel & Jun Wong, 2021. "Lender Automation and Racial Disparities in Credit Access," NBER Working Papers 29364, National Bureau of Economic Research, Inc.
    10. Ströbel, Johannes & Howell, Sabrina & Kuchler, Theresa & Snitkof, David, 2021. "Racial Disparities in Access to Small Business Credit: Evidence from the Paycheck Protection Program," CEPR Discussion Papers 16623, C.E.P.R. Discussion Papers.
    11. Jiang, Jinglin & Liao, Li & Lu, Xi & Wang, Zhengwei & Xiang, Hongyu, 2021. "Deciphering big data in consumer credit evaluation," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 28-45.
    12. José María Liberti & Mitchell A. Petersen, 2018. "Information: Hard and Soft," NBER Working Papers 25075, National Bureau of Economic Research, Inc.
    13. Yannelis, Constantine & Zhang, Anthony Lee, 2023. "Competition and selection in credit markets," Journal of Financial Economics, Elsevier, vol. 150(2).
    14. Yidi Liu & Xin Li & Zhiqiang (Eric) Zheng, 2024. "Smart Natural Disaster Relief: Assisting Victims with Artificial Intelligence in Lending," Information Systems Research, INFORMS, vol. 35(2), pages 489-504, June.
    15. Bertoletti, Lucía & Borraz, Fernando & Sanroman, Graciela, 2024. "Consumer Debt and Poverty: the Default Risk Gap," GLO Discussion Paper Series 1439, Global Labor Organization (GLO).
    16. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
    17. Dileni Gunewardena & Abdoulaye Seck, 2020. "Heterogeneity in entrepreneurship in developing countries: Risk, credit, and migration and the entrepreneurial propensity of youth and women," Review of Development Economics, Wiley Blackwell, vol. 24(3), pages 713-725, August.
    18. Goodhart, Charles, 1989. "The Conduct of Monetary Policy," Economic Journal, Royal Economic Society, vol. 99(396), pages 293-346, June.
    19. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.
    20. Zhang, Yin & Wan, Guang Hua, 2002. "Household consumption and monetary policy in China," China Economic Review, Elsevier, vol. 13(1), pages 27-52.

    More about this item

    Keywords

    credit scoring methodology; AI enabled credit scoring; AI borrower classification; responsible lending; credit scoring regulation; financial privacy; statistical discrimination;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • K12 - Law and Economics - - Basic Areas of Law - - - Contract Law
    • K23 - Law and Economics - - Regulation and Business Law - - - Regulated Industries and Administrative Law
    • K33 - Law and Economics - - Other Substantive Areas of Law - - - International Law
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:safewp:369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/csafede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.