Detecting and preventing commission and account opening overcharging by citizen science approach

Last registered on October 04, 2023


Trial Information

General Information

Detecting and preventing commission and account opening overcharging by citizen science approach
Initial registration date
September 20, 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
October 04, 2023, 1:32 PM EDT

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



Primary Investigator

Singapore Management University

Other Primary Investigator(s)

PI Affiliation
Florida International University
PI Affiliation
Singapore Management University
PI Affiliation
Singapore Management University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Mobile financial services (MFS) have rapidly widespread in the developing world. They have reached many
previously unbanked people but also created opportunities for dishonest agents to overcharge vulnerable
population. This is hard to resolve both for MFS providers and regulators because it is too costly to monitor each
agent closely and because restricting agents’ behavior may drive away some unbanked clients or induce some
agents to switch to a different MFS provider. As a potentially cost-effective way to address this difficulty, we
explore a citizen science approach using high-frequency real-time crowdsourced data, gathered by youth
volunteers, to detect agents’ overcharging behaviour. An obvious concern about crowdsourced data is the data
quality because data could be added only by crowdsource app users and even faked. Hence, we plan to utilize
mystery shoppers to verify the reliability and accuracy of the crowdsourced data. We will also test the efficacy
of an incentive treatment, where treated volunteers will be provided with additional monetary incentives to recruit citizen scientists. This enables us to obtain evidence on whether providing incentives for signing up increases the total number of citizen scientists willing to contribute to the citizen science approach and if the citizen science approach can help detect overcharging and serve as a departure point for a future scale-up randomized study.
External Link(s)

Registration Citation

Chawla, Vardaan et al. 2023. "Detecting and preventing commission and account opening overcharging by citizen science approach." AEA RCT Registry. October 04.
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Experimental Details


Our proposed treatment is that volunteers in the treatment group will receive additional incentives to recruit citizen scientists to assist in data collection. The feature to recruit citizen scientists will be open for all volunteers but only the ones in the treatment group will receive additional incentives if they get someone to sign up to be a citizen scientist.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcomes of interest include transaction-level outcomes such as (a) whether there was overcharging, (b) the amount overcharged, and (c) the customer rating of the overall experience with the agent (d) number of individuals signing up and contributing data points and the effectiveness of incentives in increasing sign-ups
Primary Outcomes (explanation)
a) overcharging outcomes will be constructed by recording how much an MFS agent charged the volunteer for a particular transaction and then comparing that with the fee schedule for that particular transaction type (e.g. account opening or sending money)
b) customer rating will be captured using a Lickert scale

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The main purpose of data collection is to collect data related to the charges imposed by MFS agents. We will engage youth volunteers from Youth Policy Forum (YPF) in Bangladesh and Network of Active Citizens (NAC) in Uganda to be citizen scientists and they will be collecting data first-hand. We will use a crowdsourcing app called POKET app, which allow us to collect georeferenced data from the app users.

This research consists of two stages of data collection. In Stage 1, we set up the basic infrastructure of data collection In Stage 0 (“pilot”), we test the POKET App in the field for 6-8 weeks. This stage is for us to gain experience before collecting data for the purpose of research. The pilot is to ensure that the app works as expected and calibrate the questionnaire if needed. When the volunteers register themselves as a user of the app, we will collect personal information about them, including their name, gender, phone number, and email address, among others, including personality traits. We will also request the volunteers to take the photograph of the signboard and surroundings in a way that does not violate anyone’s privacy. Once their information is registered, they will spend some time registering shops and MFS agents in the area before moving on to collecting transaction-level data, where transactions include opening an account as well as cash-in and cash-out of mobile money. These transaction level data can be the volunteers own actual transaction with MFS agent, simulated transaction with MFS agent (e.g. volunteers would ask about what is involved in opening the account such as necessary documents, fees (if any), forms to be filled, etc.), and interview with acquaintances who have completed transactions with MFS agents, or exit survey with people who have completed transactions with the agents. The data collected include the fees charged, the information about the MFS agents’ shop such the location of the shop and whether the fee structure was visibly displayed, and the volunteers’ assessments of the agents. In Stage 2 discussed below, we will collect the same set of information.

We refer to the first [second] half of Stage 2 as Stage 2-a [Stage 2-b], which lasts for about four weeks. In Stage 2, volunteers collect data through simulated and actual transactions as well as acquaintance interviews. Volunteers are incentivized to collect data. They will be paid based on the total number of valid data points they produce. Volunteers who were a part of data collection during Stage 1 now become team leaders as more volunteers are recruited for Stage 2. Team leaders become a point of contact for newly recruited volunteers should they face any issues in the field. They also become in-charge of relaying instructions from the research team and other implementation partners to the volunteers in the field. At the beginning of Stage 2-b, we release to all volunteers a link/QR code. The volunteers can use it to let anyone interested to become a new volunteer. When the new volunteers register themselves to be a volunteer, the referrer’s name will be asked. If a treatment volunteer successfully refers a new volunteer with the link/QR code and the new volunteer produces at least five valid data points, the treatment volunteer will be rewarded in addition to the incentives for data collection

In of Stage 2-b, we will collect mystery shopper data. We plan to engage 5 mystery shoppers in each location, who differ in the combination of the following characteristics (male/female, young/old, educated/uneducated, experienced MFS user/non-experienced MFS user). Mystery shoppers also use POKET App to collect the data and the variables collected by the mystery shopper data are essentially the same as those collected by the volunteers. However, mystery shoppers will follow the assigned script as closely as possible and conduct actual or simulated transactions. We will have about 600 mystery-shopping transactions in each location. The mystery shopper data collected in this way will allow us to see whether the observations on overcharging from mystery shopper data matches the data generated by citizen scientists (volunteers). If they match well, citizen scientists can be a useful source of information for scientific advancement. If they do not match well, we will further probe into the discrepancy. The results may be influenced by the fact that mystery shoppers are all first-time customers for the agents. Therefore, the overcharging behavior may be different. The combination of mystery shopper data and crowdsourced data collected by volunteers elucidate the relevance of citizen science approach.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Volunteers will be randomised into treatment and control groups. Stratification will be at the team level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
60 volunteers in each location
Sample size: planned number of observations
1200-4800 transactions in each location collected by volunteers, 600 mystery-shopper transactions in each location
Sample size (or number of clusters) by treatment arms
close to 50% of volunteers (~30) will be treatment volunteers and 30 control volunteers in each location
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Singapore Management University Institutional Review Board
IRB Approval Date
IRB Approval Number
IRB-21-167-A122-M2(723) & IRB-23-087-A067(723)
IRB Name
Social and Behavioral Institutional Review Board of Florida International University
IRB Approval Date
IRB Approval Number
IRB Name
Mildmay Uganda Research Ethics Committee (MUREC)
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials