---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- financial-inclusion
- fintech
- africa
- synthetic-data
- sub-saharan-africa
- mobile-money
- synthetic
size_categories:
- 10K<n<100K
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Mobile Money Adoption in Africa

Synthetic dataset modeling mobile money adoption patterns across 15 Sub-Saharan African countries from 2018-2025.

## Dataset Description

This dataset simulates individual-level mobile money adoption and usage patterns, capturing the rapid growth of mobile financial services in Africa. It reflects the transformative impact of mobile money on financial inclusion, particularly in regions with limited traditional banking infrastructure.

### Key Statistics

| Metric | Value |
|--------|-------|
| Total Records | 15,000 |
| Countries | 15 |
| Time Period | 2018-2025 |
| Mobile Money Adoption Rate | ~48% |
| Active User Rate | ~42% of account holders |
| Avg Monthly Transactions | 8-12 (active users) |
| Avg Transaction Value | $50-200 USD |

### Coverage by Scenario

- `low_burden`: 4,000 records
- `moderate_burden`: 5,000 records
- `high_burden`: 6,000 records

## Column Descriptions

| Column | Type | Description |
|--------|------|-------------|
| country | string | One of 15 SSA countries |
| year | int | Year (2018-2025) |
| urban_rural | string | Urban or rural location |
| gender | string | Male or female |
| age | int | Age in years (15-75) |
| age_group | string | Age bracket (15-24, 25-34, etc.) |
| income_quintile | string | Income group (lowest to highest) |
| account_ownership | int | Has mobile money account (0/1) |
| active_use | int | Active user in last 90 days (0/1) |
| provider | string | Mobile money provider (M-Pesa, MTN, etc.) |
| n_transactions_monthly | int | Monthly transaction count |
| avg_transaction_value_usd | float | Average transaction value in USD |
| transaction_types | string | Comma-separated transaction types |
| digital_literacy_score | int | Digital literacy (1-10 scale) |
| years_active | int | Years using mobile money |
| scenario | string | Burden scenario label |

## Usage Example

```python
import pandas as pd

# Load the combined dataset
df = pd.read_csv("mobile_money_combined.csv")

# Analyze adoption by country
adoption_by_country = df.groupby('country')['account_ownership'].mean()

# Predict active usage
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

features = ['age', 'digital_literacy_score', 'income_quintile', 'urban_rural']
X = pd.get_dummies(df[features])
y = df['active_use']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier().fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2f}")
```

## Research Sources

- GSMA State of the Industry Report on Mobile Money (SOTIR) 2025
- World Bank Global Findex Database 2021, 2025
- Central Bank reports from Kenya, Ghana, Nigeria, Uganda, Tanzania
- CGAP (Consultative Group to Assist the Poor) research publications

## Citation

```bibtex
@dataset{mobile_money_africa_2025,
  title={Mobile Money Adoption in Africa},
  year={2025},
  note={Synthetic dataset based on World Bank Findex and GSMA data}
}
```