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

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Publication History

Received: January 5, 2021
Revised: December 9, 2021; August 16, 2022; November 23, 2022
Accepted: January 11, 2023
Published Online as Articles in Advance: November 30, 2023
Published Online in Issue: December 1, 2023

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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 (i.e., “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 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 improvements. 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 mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes.

Additional Details
Author Tian Lu, Yingjie Zhang, and Beibei Li
Year 2023
Volume 47
Issue 4
Keywords Credit risk, alternative data, financial trade-off, financial inclusion, profitability, equality, biases, counterfactual simulated experiment
Page Numbers 1517-1556
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