Survey intervention to prevent financial fraud

Last registered on June 06, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

Survey intervention to prevent financial fraud

RCT ID

AEARCTR-0009503

Initial registration date

June 05, 2022

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

June 06, 2022, 5:12 AM EDT

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

Locations

Country

China

Region

Primary Investigator

Name

Michaela Pagel

Affiliation

Columbia Business School

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Shang-Jin Wei

PI Affiliation

Columbia Business School

Contact Investigator

PI Name

Zhuan Xie

PI Affiliation

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-06-02

End date

2022-07-31

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 want to better understand whether people can avoid financial fraud when they are more alert and educated about it.

External Link(s)

Registration Citation

Citation

Pagel, Michaela, Shang-Jin Wei and Zhuan Xie. 2022. "Survey intervention to prevent financial fraud." AEA RCT Registry. June 06. https://doi.org/10.1257/rct.9503-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The purpose is to investigate to what extent people are able to make correct decisions in situations where they are presented with attempts to financial fraud. The study will yield insights about financial fraud and the role of trust in economic decision making.

Intervention Start Date

2022-06-02

Intervention End Date

2022-07-31

Primary Outcomes

Primary Outcomes (end points)

The outcome variable will be the incidence of financial fraud as measured by users reporting financial fraud to the digital payments platform and by the platform intervening transactions or sending people alerts about transactions that the platform perceives to be likely financial fraud.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We run a survey experiment to prevent financial fraud.

Experimental Design Details

We partner with one of the largest Chinese digital payment platforms. The platform will send users a survey asking questions about financial literacy and financial fraud. Participants are offered to answer the survey as an ad on their app's home screen or in their message inbox. They answer questions for about 6 to 10 minutes. Participants are randomly assigned to 4 different surveys, half of them containing questions about financial fraud.
An invitation ad will be posted on the app's front page or users will find an invitation in their app's inbox. The link will take participants to the survey/questionnaire website.

Randomization Method

Step 1: Use the number of logins of past six month (Dec. 11th, 2021 to May 10th, 2022) to select active Alipay users. If number of logins >=100, the user will be selected.
Step2: Drop a small fraction of users for who the information on gender, province code, or age (if less than 16) is missing.
Step3: Stratifying the population and sampling.
Use class of city (6 classes), gender (2 types), age (4 cohorts, 15-24, 25-39, 40-59, 60+) expenditure rank (5 ranks according to total consumption of Nov. 2021 to Apr. 2022), binary indicator of investment on the platform to stratify the population into groups.
For each user in each group, randomly assign a number one to four to answer our four questionnaires.

Randomization Unit

Individual active user

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

We expect to send surveys to 960K users and 36K users in each strata. Then, depending on the click-on-the-survey and complete-the-survey rates, will end up with 100,000 to 500,000 subjects.

Sample size (or number of clusters) by treatment arms

Depending on the click-on-the-survey and complete-the-survey rates, we will end up with 100,000 to 500,000 subjects, half of which will be treated.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We are interested in the simple difference between the treated-with-financial-fraud-survey groups and treated-with-other-survey groups in terms of the incidence rate of financial fraud (as measured by reported complaints or interrupted or flagged transactions). The type-1 error rate should be less than 5% and the power 80%. If the baseline rate of financial fraud in the control group is 5%, then, in order to detect a 10% difference, i.e., a decrease to 0.45% due to the treatment, we need a sample size of 49,202 subjects (half treated and half control).

Supporting Documents and Materials

IRB