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

MIS Quarterly, Forthcoming

Posted: 18 Feb 2021 Last revised: 18 Jan 2023

See all articles by Tian Lu

Tian Lu

Department of Information Systems, Arizona State University

Yingjie Zhang

Peking University - Guanghua School of Management

Beibei Li

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

Date Written: January 17, 2023

Abstract

The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (a.k.a. “alternative data”) stemming from the high penetration of mobile devices and Internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel “meta” experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and likewise, nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvement. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help to mitigate statistical bias driven by the unobserved factors or under-representative training samples in machine-based risk assessment processes.

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

Suggested Citation

Lu, Tian and Zhang, Yingjie and Li, Beibei, Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective on Alternative Data (January 17, 2023). MIS Quarterly, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3758120 or http://dx.doi.org/10.2139/ssrn.3758120

Tian Lu (Contact Author)

Department of Information Systems, Arizona State University ( email )

Tempe, AZ 85287
United States

HOME PAGE: http://isearch.asu.edu/profile/tianlu1

Yingjie Zhang

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Beibei Li

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

Pittsburgh, PA 15213-3890
United States

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