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Can robo-advice help with debt repayment?
Last registered on September 18, 2020

Pre-Trial

Trial Information
General Information
Title
Can robo-advice help with debt repayment?
RCT ID
AEARCTR-0006447
Initial registration date
September 17, 2020
Last updated
September 18, 2020 10:31 AM EDT
Location(s)
Primary Investigator
Affiliation
Financial Conduct Authority
Other Primary Investigator(s)
PI Affiliation
Boston College
PI Affiliation
Georgetown
PI Affiliation
Boston College
PI Affiliation
Financial Conduct Authority
PI Affiliation
Financial Conduct Authority
PI Affiliation
Financial Conduct Authority
Additional Trial Information
Status
In development
Start date
2020-09-18
End date
2021-12-31
Secondary IDs
Abstract
We have concerns that consumers can find it difficult to make good decisions across a wide range of financial products. This is particularly true in the context of debt management, where where through poor repayment and refinancing decisions, debts can increase and few consumers seek out advice until they are in serious financial difficulty. To address this situation, it is important to understand better how consumers make decisions and the extent to which well-timed and appropriate advice can help.

In this work, we will focus on the debt repayment and explore the potential for automated robo-advice to assist consumers to make better decisions. A number of firms are starting to provide these services. At present the majority of debt advice is provided through a mix of charitable and for-profit organisations but debt charities do not have sufficient capacity to meet consumer demand and consumers may be unwilling or unable to pay for for-profit assistance. Robo-advice has a potential to address this and may provide additional benefits when consumers feel uncomfortable discussing their debt issues in person with a human advisor. However, very little is known about the effectiveness of this advice, whether people would be willing to trust or take up automated advice and what they would be willing to pay for it.

We propose an online trial to investigate consumer outcomes and attitudes to different types of robo-advice for debt management. In this experiment, participants will face a number of hypothetical scenarios where they have a certain amount of money to allocate to the repayment of multiple debts. The objective of the participant is to allocate the repayments in such a way as to minimise the interest and fees they would pay.
External Link(s)
Registration Citation
Citation
Burke, Chris et al. 2020. "Can robo-advice help with debt repayment?." AEA RCT Registry. September 18. https://doi.org/10.1257/rct.6447-1.0.
Experimental Details
Interventions
Intervention(s)
We propose an online trial to investigate consumer outcomes and attitudes to different types of robo-advice for debt management. In this experiment, participants will face a number of hypothetical scenarios where they have a certain amount of money to allocate to the repayment of multiple debts. In some of the scenarios they will be offered robo-advice, possibly with a training element. The objective of the participant is to allocate the repayments in such a way as to minimise the interest and fees they would pay.
Intervention Start Date
2020-09-18
Intervention End Date
2020-10-30
Primary Outcomes
Primary Outcomes (end points)
Our primary outcome measure is the amount of hypothetical interest and fees the individuals saves relative to the optimal and most suboptimal repayment strategies.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
A secondary outcome measure is the length of time spent on each task.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Randomisation will take place at the individual level. Participants will be randomly allocated to the control group or one of the treatment arms.
Experimental Design Details
Not available
Randomization Method
Randomisation will be achieved using the randomizer built into the survey software we will use (Qualtrics).
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
4500 individuals
Sample size: planned number of observations
4500 individuals
Sample size (or number of clusters) by treatment arms
900 individuals control, 900 individuals in each of four treatment arms (robo-advice with and without education and with and without willingness to pay)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Financial Conduct Authority Internal Ethics Review Board
IRB Approval Date
2020-05-06
IRB Approval Number
N/A