We estimate the cost of cultural biases in high-stake economic decisions by comparing agents' peer-to-peer lending choices with those the same agents make under the assistance of an automated robo-advisor. We first confirm substantial in-group vs. out-group and stereotypical discrimination,...

We document five effects of providing individuals with crowdsourced spending information about their peers (individuals with similar characteristics) through a FinTech app. First, users who spend more than their peers reduce their spending significantly, whereas users who spend less keep...

In this chapter, we first discuss the limitations of traditional financial advice, which led to the emergence of robo-advising. We then describe the main features of robo-advising and propose a taxonomy of robo-advisors based on four defining dimensions - personalization, discretion,...

We study a robo-advising portfolio optimizer that constructs tailored strategies based on investors' holdings and preferences. Adopters are similar to non-adopters in terms of demographics, but have more assets under management, trade more, and have higher risk-adjusted performance. The...

Poor debt-management skills lower financial security and wealth accumulation. Because optimal solutions to credit repayment problems depend on neither risk preferences nor beliefs, loan repayment is a prime application for robo-advising. Vulnerable households, though, tend to distrust new...

We analyze the relation between the location of a pension fund in its network and the investment performance, risk taking, and flows of the fund. Our approach analyzes the centrality of the fund’s management company by examining the number of connections it has with other management companies...