Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective of Alternative Data

59 Pages Posted: 18 Feb 2021

See all articles by Tian Lu

Tian Lu

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Yingjie Zhang

University of Texas at Dallas - Naveen Jindal School of Management

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Date Written: December 31, 2020

Abstract

While it is well noted the importance of pursuing inclusive finance to accelerate economic growth and financial sustainability, few actionable insights have been developed toward how financial institutions can balance the potential socioeconomic trade-off between equality and profitability. The challenges are largely three-folds: (1) lack of sufficient information (collateral); (2) lack of understanding of the impact of information on financial equality beyond economic profitability; and (3) lack of real-world causal evaluations under a (large) alternative set of counterfactual policies. Enlightened by the emerging sources of digitized user-behavior data (a.k.a. “alternative data”) enabled by the high penetration of mobile devices and internet access, we study how alternative data from smartphones and social media can help mitigate the potential financial inequality while preserving business profitability in the context of financial credit risk assessment. For evaluation, we partnered with a leading microloan platform and designed a “meta” field experiment that allowed us to recover and compare the “what-if” scenarios in the real world under an exhaustive set of counterfactual policies. Interestingly, we find that compared to using online social media information, profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion (an increase of 23.05% vs. 18.11%), and likewise 1.3 times more effective in improving business profitability (an increase of 42% vs. 33%). Surprisingly, using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigate the potential underlying mechanism for improved financial inclusion. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear more orthogonal to conventional sensitive demographic attributes. This, in turn, can help better mitigate the statistical bias driven by unobserved factors or under-representative training samples during machine-based risk assessment processes.

Keywords: Credit Risk, Alternative Data, Financial Trade-off, Financial Inclusion, Profitability, Equality, Biases, Meta Experiment

Suggested Citation

Lu, Tian and Zhang, Yingjie and Li, Beibei, Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective of Alternative Data (December 31, 2020). Available at SSRN: https://ssrn.com/abstract=3758120 or http://dx.doi.org/10.2139/ssrn.3758120

Tian Lu (Contact Author)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Yingjie Zhang

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
32
Abstract Views
194
PlumX Metrics