Hakki Yazici
Slavík Ctirad
Kina Özlem
Tilman Graff
Tilman Graff
Yuri Ostrovsky
Martin Munk
Anton Heil
Maitreesh Ghatak
Robin Burgess
Oriana Bandiera
Claire Balboni
Jonna Olsson
Richard Foltyn
Minjie Deng
Iiyana Kuziemko
Elisa Jácome
Juan Pablo Rud
Bridget Hofmann
Sumaiya Rahman
Martin Nybom
Stephen Machin
Hans van Kippersluis
Anne C. Gielen
Espen Bratberg
Jo Blanden
Adrian Adermon
Maximilian Hell
Robert Manduca
Robert Manduca
Marta Morazzoni
Aadesh Gupta
David Wengrow
Damian Phelan
Amanda Dahlstrand
Andrea Guariso
Erika Deserranno
Lukas Hensel
Stefano Caria
Vrinda Mittal
Ararat Gocmen
Clara Martínez-Toledano
Yves Steinebach
Breno Sampaio
Joana Naritomi
Diogo Britto
François Gerard
Filippo Pallotti
Heather Sarsons
Kristóf Madarász
Anna Becker
Lucas Conwell
Michela Carlana
Katja Seim
Joao Granja
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Steven J. Davis
Nicholas Bloom
José María Barrero
Thomas Sampson
Adrien Matray
Natalie Bau
Darryl Koehler
Laurence J. Kotlikoff
Alan J. Auerbach
Irina Popova
Alexander Ludwig
Hakki Yazici
Slavík Ctirad
Kina Özlem
Tilman Graff
Tilman Graff
Yuri Ostrovsky
Martin Munk
Anton Heil
Maitreesh Ghatak
Robin Burgess
Oriana Bandiera
Claire Balboni
Jonna Olsson
Richard Foltyn
Minjie Deng
Iiyana Kuziemko
Elisa Jácome
Juan Pablo Rud
Bridget Hofmann
Sumaiya Rahman
Martin Nybom
Stephen Machin
Hans van Kippersluis
Anne C. Gielen
Espen Bratberg
Jo Blanden
Adrian Adermon
Maximilian Hell
Robert Manduca
Robert Manduca
Marta Morazzoni
Aadesh Gupta
David Wengrow
Damian Phelan
Amanda Dahlstrand
Andrea Guariso
Erika Deserranno
Lukas Hensel
Stefano Caria
Vrinda Mittal
Ararat Gocmen
Clara Martínez-Toledano
Yves Steinebach
Breno Sampaio
Joana Naritomi
Diogo Britto
François Gerard
Filippo Pallotti
Heather Sarsons
Kristóf Madarász
Anna Becker
Lucas Conwell
Michela Carlana
Katja Seim
Joao Granja
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Steven J. Davis
Nicholas Bloom
José María Barrero
Thomas Sampson
Adrien Matray
Natalie Bau
Darryl Koehler
Laurence J. Kotlikoff
Alan J. Auerbach
Irina Popova
Alexander Ludwig

Predictably unequal? The effect of machine learning on credit markets

What is this research about and why did you do it?

The use of machine learning in credit allocation should allow lenders to better extend credit, but the shift from traditional to machine learning lending models may have important distributional effects for consumers. Our study analyzes the effect of machine learning on mortgage lending in the US. It finds that machine learning would offer lower rates to racial groups who already benefited from advantageous rates under the traditional model thus exacerbating distributional effects, but it would also benefit disadvantaged groups by enabling them to obtain a mortgage in the first place.

How did you answer this question?

We build simple theoretical frameworks to better understand the issues involved, and empirically estimate the likely impacts using a large administrative dataset from the US mortgage market, comprising about 10 million mortgage loans made between 2009 and 2013. In this setting, we compare the predictions that a hypothetical lender would make when using traditional statistics (e.g. standard Logit models) to those when using supervised machine learning techniques such as the Random Forest and XGBoost.

What did you find?

Figure 1 shows one of our key results. On the horizontal axis is the change in the log predicted default probability as lenders move from traditional technology (“Logit”) to machine learning (“Random Forest”). On the vertical is the cumulative share of borrowers from each racial group that experience a given level of change. Borrowers to the left of the solid vertical line are “winners” who are classed as less risky by the more sophisticated algorithm than by the traditional model. About 65% of White Non-Hispanic and Asian borrowers win, compared with about 50% of Black and Hispanic borrowers. The gains from new technology are therefore skewed in favour of racial groups that already enjoy socio-economic advantages.

The paper goes further in showing that these effects are driven mostly by the flexibility of new technology, as opposed to by machine learning algorithms’ ability to essentially proxy for, or triangulate, borrowers’ race. We also decompose the equilibrium effects of changes in statistical technology in a model of competitive credit provision.

What implications does this have for the study on wealth concentration or economic inequality?

Our research has taken a first step towards a deeper understanding of the problems associated with the widespread adoption of new machine learning technologies. In the US mortgage market, we find that concerns about unequal effects are indeed valid. Perhaps more importantly, however, we lay out a framework for assessing and decomposing such effects, which can be applied beyond our dataset.

What are the next steps in your agenda?

Further exploration of how technological innovation in finance can affect inequality.

Citation

This paper can be cited as follows: Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., and Walther, A. (2022) "Predictably unequal? The effects of machine learning on credit markets." The Journal of Finance, 77(1), pp. 5-47.

About the authors