Abstract
Robo-advice uses big and open data to provide consumers with fully informed and rational-expectation benchmarks in all realms of household finance, including consumption, saving, investment, and debt management choices. It also minimizes the monetary, cognitive, and psychological costs that households face in economic transactions. We review recent research on the features and effects of robo-advice on individual and aggregate economic outcomes through the lens of its differences from traditional human advice. We discuss the distributional implications of robo-advice, its potential role in increasing the effectiveness of economic policies, the role of providers’ incentives, and several questions that are still wide open for researchers in finance, economics, social psychology, and related fields.
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