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electricsheepafrica
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global-findex-database-2025

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
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financial-inclusion
world-bank
survey-data
global
economics
tabular
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global-findex-database-2025 / README.md
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metadata
language:
  - en
license: cc-by-4.0
tags:
  - financial-inclusion
  - world-bank
  - survey-data
  - global
  - economics
  - tabular
pretty_name: Global Findex Database 2025
size_categories:
  - 1K<n<10K
task_categories:
  - tabular-classification
  - tabular-regression

Global Findex Database 2025

Dataset Description

The Global Financial Inclusion (Global Findex) Database is the world's most comprehensive dataset on financial inclusion. Published by the World Bank, it provides nationally representative survey data on how adults save, borrow, make payments, and manage risk.

Key Statistics

Metric Value
Rows 8,564
Columns 437
Countries 174
Years 2011, 2014, 2017, 2021, 2022, 2024
Indicators ~300 financial inclusion metrics

Coverage

Geographic

  • 174 countries and economies
  • All World Bank regions
  • All income groups (Low, Lower-middle, Upper-middle, High)

Demographic Breakdowns

Data is disaggregated by:

  • Gender: men, women
  • Income: richest 60%, poorest 40%
  • Age: ages 15-24, age 25+
  • Location: rural, urban
  • Labor force: in/out of workforce
  • Education: primary or less, secondary or more

Indicator Categories

  • Account ownership (bank, mobile money)
  • Digital payments
  • Saving behavior
  • Borrowing and credit
  • Financial resilience
  • Mobile phone and internet access

Usage 

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("electricsheepafrica/global-findex-database-2025")

# Convert to pandas
df = dataset["train"].to_pandas()

# Filter to specific country and year
nigeria_2024 = df[(df['country'] == 'Nigeria') & (df['year'] == 2024)]

# Get all indicators for women
women_data = df[df['group2'] == 'women']

With Pandas directly 

import pandas as pd
df = pd.read_parquet("hf://datasets/electricsheepafrica/global-findex-database-2025/global_findex_2025.parquet")

Data Structure

Metadata Columns

Column Description
country Country name
country_code ISO 3-letter code
year Survey year
pop_adult Adult population
region World Bank region
income_group Income classification
group Demographic dimension (gender, age, income, etc.)
group2 Demographic subgroup value

Indicator Columns

All 429 indicator columns contain proportions (0-1) representing the share of adults meeting each criterion. See DATA_DICTIONARY.md for full details.

Sample indicators:

  • account_t_d - Has an account (% age 15+)
  • fiaccount_t_d - Has a financial institution account
  • mobileaccount_t_d - Has a mobile money account
  • borrow_any_t_d - Borrowed any money (% age 15+)

Suggested ML Tasks

  1. Financial inclusion prediction - Predict account ownership from country characteristics
  2. Time series analysis - Track financial inclusion trends 2011-2024
  3. Clustering - Segment countries by financial inclusion patterns
  4. Gap analysis - Identify gender/income gaps in financial access
  5. Regional comparison - Compare financial inclusion across regions

Source

Citation 

@misc{global_findex_2025,
  title={Global Findex Database 2025},
  author={{World Bank}},
  year={2025},
  publisher={World Bank},
  url={https://www.worldbank.org/en/publication/globalfindex}
}

Documentation