Improving Household Debt Management with Robo-Advice

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 technologies and override suggestions that do not align with ingrained heuristics, such as matching the minimum payment on a credit card balance. Lower adoption rates by these groups might increase rather than reduce wealth inequalities. To assess these trade-offs, we design and implement an RCT in which robo-advice for borrower repayment decisions is offered to a set of representative UK consumers. The availability of free robo-advice significantly improves average loan repayment choices. When their willingness to pay is elicited, many subjects report values larger than the monetary benefits of the tool, perhaps due to lower cognitive and psychological costs decision-makers face when making assisted choices. Non-adopters and overriders report lower trust in algorithms at the end of the experiment. We find no evidence of learning from robo-advice, which barely improves subsequent unassisted choices, even when paired with explicit tips. In fact, robo-advice usage crowds out learning-by-doing, which is highest for those who make all choices unassisted.