How to Combat Cyber Scams? A Randomized Control Trial on a Major Online Payment Platform in China

Last registered on August 10, 2023
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Pre-Trial

Trial Information

General Information

Title
How to Combat Cyber Scams? A Randomized Control Trial on a Major Online Payment Platform in China
RCT ID
AEARCTR-0011899
Initial registration date
August 06, 2023

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
August 10, 2023, 1:30 PM 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
Juanjuan Meng
Affiliation
Peking University
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Wei Li
PI Affiliation
Peking University and Hong Kong University

Additional Trial Information

Status
In development
Start date
2023-08-13
End date
2023-08-18
Keywords
Behavior
Additional Keywords
consumer protection, scam education, behavior intervention
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In the rapid evolution of the digital economy, the escalating severity of online fraud has spurred widespread concern, prompting the exploration of effective countermeasures. This paper plans to collaborate with a major online payment platform in China and run a randomized control trial. We will explore in the context of online payment, what is the most effective intervention in aiding users to recognize scams and reducing the incidence rate of online fraud. We will also analyse the heterogeneous effects of various interventions on different population.
External Link(s)

Registration Citation

Citation
Li, Wei and Juanjuan Meng. 2023. "How to Combat Cyber Scams? A Randomized Control Trial on a Major Online Payment Platform in China." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.11899-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
1. General risk reminder: presents a uniform interface and standardized messaging to all users.
2. Personalized risk reminder: provides tailored warnings based on user-selected transaction purposes and recipient information, explicitly highlighting potential fraud scenarios.
3. Voice-based risk reminder: allows users to choose whether to accept voice notifications. Upon acceptance, an AI-powered customer service representative provides risk-related information through voice prompts.
4. Payment Cooling-off Period: implementing a typically 15-minute waiting period, during which users can't proceed with the payment. Users can undergo identity verification and manual review to bypass the cooling-off period.
5. Disengageable Payment Interception: implementing an initial interception of a payment. After the user confirms and verifies a series of information, the interception is lifted, allowing the payment to proceed.
Intervention Start Date
2023-08-13
Intervention End Date
2023-08-18

Primary Outcomes

Primary Outcomes (end points)
whether the transaction is successful; whether the user has lodged fraud complaints with the payment platform; the amount involved in each fraud case.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment will be run on a major online payment platform in China.

We have five experimental groups based on the different intervention:
1. General risk reminder
2. Personalized risk reminder
3. Voice-based risk reminder
4. Payment Cooling-off Period
5. Disengageable Payment Interception

The subjects are divided into two groups. One group consists of individuals with originally low-risk profiles who have not triggered backend intervention mechanisms, serving as the control. The other group comprises individuals at risk whose payment behaviors have triggered intervention mechanisms. Different types of interventions will be randomly assigned within each of these two groups.
Experimental Design Details
Randomization Method
done in office by a computer
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 150,000 individuals
Sample size: planned number of observations
About 150,000 individuals
Sample size (or number of clusters) by treatment arms
About 30,000 individuals for each treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials