The impact of machine learning and big data on credit markets
Peter Eccles (),
Paul Grout (),
Paolo Siciliani () and
Anna Zalewska ()
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Peter Eccles: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Paul Grout: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Paolo Siciliani: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Anna Zalewska: University of Bath
No 930, Bank of England working papers from Bank of England
There is evidence that machine learning (ML) can improve the screening of risky borrowers, but the empirical literature gives diverse answers as to the impact of ML on credit markets. We provide a model in which traditional banks compete with fintech (innovative) banks that screen borrowers using ML technology and show that the impact of the adoption of the ML technology on credit markets depends on the characteristics of the market (eg borrower mix, cost of innovation, the intensity of competition, precision of the innovative technology, etc.). We provide a series of scenarios. For example, we show that if implementing ML technology is relatively expensive and lower-risk borrowers are a significant proportion of all risky borrowers, then all risky borrowers will be worse off following the introduction of ML, even when the lower-risk borrowers can be separated perfectly from others. At the other extreme, we show that if costs of implementing ML are low and there are few lower-risk borrowers, then lower-risk borrowers gain from the introduction of ML, at the expense of higher-risk and safe borrowers. Implications for policy, including the potential for tension between micro and macroprudential policies, are explored.
Keywords: Adverse selection; banking; big data; capital requirements; credit markets; fintech; machine learning; prudential regulation (search for similar items in EconPapers)
JEL-codes: G21 G28 G32 (search for similar items in EconPapers)
Pages: 930 pages
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cfn, nep-cmp, nep-fmk, nep-pay and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0930
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