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Data

Active mobile money accounts

See all data and research on:
Technological Change

What you should know about this indicator

  • Mobile money accounts are financial accounts managed via mobile devices. They offer services like deposits, transfers, and payments, mainly in regions with limited banking access.
  • In this data, accounts are considered active when they have been used to perform at least one mobile money payment during the last 90 days of each year.
  • North America is not shown because mobile money accounts are not used across this region.

How is this data described by its producer?

The number of customer accounts that have been used to perform at least one P2P payment, bill payment, bulk payment, cash in to account, cash out from account, merchant payments, international remittances or airtime top up from account during at least 90 days prior to end of the indicated months. Balance inquiries, PIN resets, and other transactions that do not involve the movement of value DO NOT qualify a customer account as active.

Active mobile money accounts
Mobile money accounts used to perform at least one mobile money payment during the last 90 days of each year.
Source
GSM Association (2025) – with minor processing by Our World in Data
Last updated
March 9, 2026
Next expected update
March 2027
Date range
2010–2024
Unit
active accounts

Related research and writing

There are now more than half a billion mobile money accounts in the world, mostly in Africa — here's why this matters

Simon van Teutem

Sources and processing

This data is based on the following sources

GSM Association – Global Mobile Money Dataset

The Global Mobile Money Dataset is a set of global metrics on mobile money accounts, collected by the GSM Association (GSMA). It employs a wide range of data sources that the Mobile Money program has been collecting and analyzing since 2009.

Retrieved on
March 9, 2026
Retrieved from
https://www.gsma.com/mobilemoneymetrics/#global
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Global Mobile Money Dataset, GSM Association

The Global Mobile Money Dataset is a set of global metrics on mobile money accounts, collected by the GSM Association (GSMA). It employs a wide range of data sources that the Mobile Money program has been collecting and analyzing since 2009.

Retrieved on
March 9, 2026
Retrieved from
https://www.gsma.com/mobilemoneymetrics/#global
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Global Mobile Money Dataset, GSM Association

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Active mobile money accounts”, part of the following publication: Max Roser, Hannah Ritchie, and Edouard Mathieu (2023) - “Technological Change”. Data adapted from GSM Association. Retrieved from https://archive.ourworldindata.org/20260327-114717/grapher/active-mobile-money-accounts.html [online resource] (archived on March 27, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

GSM Association (2025) – with minor processing by Our World in Data

Full citation

GSM Association (2025) – with minor processing by Our World in Data. “Active mobile money accounts” [dataset]. GSM Association, “Global Mobile Money Dataset” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260327-114717/grapher/active-mobile-money-accounts.html (archived on March 27, 2026).

Download

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/active-mobile-money-accounts.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/active-mobile-money-accounts.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/active-mobile-money-accounts.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/active-mobile-money-accounts.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/active-mobile-money-accounts.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/active-mobile-money-accounts.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/active-mobile-money-accounts.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/active-mobile-money-accounts.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear