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.

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利润与平等？金融风险评估案例和另类数据的新视角


追求金融包容性对于加速经济增长和增强金融可持续性的重要性已得到充分关注。然而，关于金融机构如何平衡盈利能力和平等之间潜在的社会经济权衡，研究几乎没有提供可操作的见解。一项主要挑战源于缺乏对各种类型市场信息对经济盈利能力以外的财务平等的影响的了解。另一个挑战在于如何评估现实世界环境中大量反事实政策下的社会经济权衡。我们本研究的动机是源于移动设备和互联网接入的高普及率的数字化用户行为数据（即“替代数据”）的新兴来源。因此，我们研究了来自智能手机和社交媒体的替代数据如何帮助减轻潜在的金融不平等，同时在金融信用风险评估的背景下保持企业盈利能力。我们与一家领先的小额贷款网站合作，设计了一种新颖的“元”实验，使我们能够在一套详尽的反事实政策下模拟各种现实世界的现场实验。有趣的是，我们发现使用智能手机活动分析用户财务风险在改善普惠金融方面的效果比使用在线社交媒体信息高 1.3 倍（高 23.05% vs. 18.11%），在提高企业盈利能力方面高近 1.3 倍（高 42%）比 33% 更好）。令人惊讶的是，我们发现利用消费者的在线购物活动进行信用风险分析可能会损害金融普惠性。此外，我们还调查了普惠金融改善的潜在解释。我们的研究结果表明，替代数据，尤其是用户的智能手机活动，不仅表现出更高的普遍性，而且似乎与传统的敏感人口统计属性更加正交。反过来，这可以帮助减轻基于机器的风险评估过程中由未观察到的因素或代表性不足的训练样本引起的统计偏差。