We document that, since 2011, mortgage lenders reduced credit to middle-class households by 15% and increased credit to wealthy households by 21%. Credit to low-income households was unaffected. Results hold at the individual-loan level and zip-code level, and at the intensive margin and...

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...

We study the effects of goal-setting on saving behavior, exploiting the exogenous introduction of goal-setting features for saving in a FinTech app. We establish that setting goals increases individuals' saving rate, and show that the effect is causal using two distinct identification strategies...

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 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...

We investigate the transformational role of FinTech and robo-advising---just-in-time scalable and portable advice for financial decision-making---in the personal finance industry. We start by describing the unique features that differentiate FinTech and robo-advising from traditional technology...