Leveraging Technology to Enhance Access to Financial Advice: Trust, Uptake, and Advisor Adoption

Last registered on May 18, 2026
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Pre-Trial

Trial Information

General Information

Title
Leveraging Technology to Enhance Access to Financial Advice: Trust, Uptake, and Advisor Adoption
RCT ID
AEARCTR-0018280
Initial registration date
May 15, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
May 18, 2026, 7:52 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Philippe d'Astous
Affiliation
HEC Montreal
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Other Primary Investigator(s)

PI Name
Maximilian Voigt
PI Affiliation
HEC Montreal
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Additional Trial Information

Status
In development
Start date
2026-05-20
End date
2026-12-31
Keywords
Behavior, Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study how access to generative artificial intelligence (AI) tools affects the quality of financial advice and consumer investment decisions using a lab-in-the-field experiment. The experiment has two stages. In Stage 1, Canadian certified financial planners (FPs) are randomly assigned to advise hypothetical clients with or without access to a generative AI tool. Client profiles vary by age, gender, and risk preferences. FPs observe asset price developments. The recommend portfolio allocations across five risky assets with known return distributions and a risk-free asset with zero return at two decision points. In Stage 2, customers observe the same asset price development and make portfolio allocation decisions across the same assets. Customers are randomly assigned to one of four treatment arms: no advice, advice from an FP without access to AI, advice from an FP with access to AI, and advice generated directly by a generative AI.
External Link(s)

Registration Citation

Citation
d'Astous, Philippe and Maximilian Voigt. 2026. "Leveraging Technology to Enhance Access to Financial Advice: Trust, Uptake, and Advisor Adoption." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.18280-1.0
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Experimental Details

Interventions

Intervention(s)
In Stage 1—for financial planners (FPs)—we exogenously vary whether the FP giving advice can access a generative AI tool or not. In Stage 2—for customers—we exogenously vary if they can obtain financial advice as well as the source of financial advice.
Intervention Start Date
2026-05-20
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
Our key outcome variable is a vector of wealth shares allocated or recommended to be allocated to each of the five risky assets and a risk-free asset at each decision point.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Our secondary outcome variables are (a) beliefs about the expected return of each asset, (b) the decision to access financial advice for customers or to access the generative AI for FPs, and (c) the textual description of the financial advice given. In addition, we elicit a non-incentivized trust measure using the amount sent during a standard trust game.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct a standard portfolio choice experiment as in Camerer and Weber (1998) or Fischbacher, Hoffmann, and Schudy (2017). Financial planners (FPs) as well as customers see the same development of asset prices. While FPs recommend a portfolio allocation for hypothetical customers at two decision points, the customers choose their portfolio allocation over ten periods.
Experimental Design Details
Not available
Randomization Method
Computer-generated randomization. Asset-price paths are pre-generated using a fixed random seed. The assignment of FPs to client profiles, paths, and AI access is pre-generated in balanced blocks using computer randomization. Each asset-price path is randomly assigned to a subset of the 18 customer-profile cells, creating a sparse but connected bipartite assignment graph. This ensures joint identification of cell and path fixed effects in an additive two-way framework.
Customers assignment to price paths and treatment arms is randomized within profile cells at the time of participation.
Randomization Unit
Individual. In Stage 1, the individual financial planner is the unit of randomization for AI access. In Stage 2, the individual consumer is the unit of randomization for treatment arm and path assignment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (individual-level randomization). Approximately 1,000 financial planners and 2,000 consumers.
Sample size: planned number of observations
We target approximately 1,000 financial planners (Stage 1) and 2,000 consumers (Stage 2), for a total of 3,000 participants.
Sample size (or number of clusters) by treatment arms
Stage 1: approximately 500 FPs with AI access, 500 FPs without AI access.
Stage 2: approximately 500 consumers per arm (control, FP advice, FP+AI advice, ChatGPT advice).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Ethics Board (REB) of HEC Montréal
IRB Approval Date
2026-04-01
IRB Approval Number
2026-6749
Analysis Plan