electricsheepafrica/african-mobile-insurance-adoption

---
license: cc-by-4.0
task_categories:
- tabular-classification
tags:
- insurance
- mobile
- microinsurance
- mobile-money
- fintech
- sub-saharan-africa
- synthetic
- adoption
pretty_name: African Mobile Insurance Adoption
size_categories:
- n<10K
language:
- en
configs:
- config_name: baseline
data_files: data/mobile_insurance_baseline.csv
- config_name: rapid_growth
data_files: data/mobile_insurance_rapid_growth.csv
- config_name: matured_market
data_files: data/mobile_insurance_matured_market.csv
---

# African Mobile Insurance Adoption

Synthetic mobile insurance adoption dataset covering 12 Sub-Saharan African countries across 3 market scenarios. Designed for fintech adoption research, customer segmentation, product-market fit analysis, and insurance penetration studies.

## Dataset Description

- **10,000** synthetic mobile insurance adoption records
- **12 countries**: South Africa, Nigeria, Kenya, Ghana, Tanzania, Uganda, Rwanda, Ethiopia, Senegal, Cote d'Ivoire, Zambia, Mozambique
- **3 scenarios**: baseline, rapid_growth, matured_market
- **22 variables** per record

## Variables

| Variable | Type | Description |
|---|---|---|
| user_id | string | Unique user identifier |
| country | string | Country of origin |
| region | string | Sub-region (East, West, Southern) |
| year | int | Observation year |
| age | int | User age (18-75) |
| gender | string | gender_male, gender_female, gender_other |
| income_bracket | string | Income level (low, lower_middle, upper_middle, high) |
| education_level | string | Education (none, primary, secondary, tertiary, postgraduate) |
| occupation | string | Occupation category |
| mobile_money_user | int | Binary: uses mobile money |
| mobile_money_monthly_vol_usd | float | Monthly mobile money volume in USD |
| has_smartphone | int | Binary: owns smartphone |
| insurance_awareness | int | Awareness score (0-10) |
| insurance_trust_score | int | Trust in insurance (0-10) |
| previous_insurance | int | Binary: had insurance before |
| premium_affordability_score | int | Affordability score (0-10) |
| claim_ease_expectation | int | Expected claim ease (0-10) |
| mobile_insurance_awareness | int | Awareness of mobile insurance (0-10) |
| adoption_stage | string | aware, interested, considered, applied, active, churned |
| product_type | string | Product type (hospital_cash, life_cover, device_protection, accident, funeral, combined) |
| monthly_premium_usd | float | Monthly premium in USD |
| channel | string | Acquisition channel (mobile_app, ussd, agent, bank, employer) |
| scenario | string | baseline, rapid_growth, or matured_market |

## Scenarios

- **baseline** (4K records): Early market adoption, 2022-2024. Low awareness, gradual adoption.
- **rapid_growth** (4K records): Rapid market expansion, 2024-2025. Increased awareness, aggressive acquisition.
- **matured_market** (2K records): Mature market conditions, 2025-2026. Higher trust, product diversity.

## Generation Methodology

Parameters are calibrated against published data from:
- GSMA State of the Industry Report on Mobile Money 2025
- Global Findex Database 2025
- World Bank Global Findex microinsurance data
- Microinsurance Network 2025 trends
- Deloitte Africa Insurance Outlook 2024/2025
- Sanlam Microinsurance research for Sub-Saharan Africa
- AFI Mobile Insurance for Financial Inclusion reports

Adoption stages follow multinomial distributions modulated by country-specific mobile money penetration, digital literacy, and insurance trust scores.

## Use Cases

- Mobile insurance adoption modeling
- Customer segmentation analysis
- Product-market fit research
- Channel effectiveness optimization
- Cross-country adoption comparison
- Synthetic data generation evaluation

## Citation

```bibtex
@dataset{african_mobile_insurance_2026,
title={African Mobile Insurance Adoption Dataset},
author={ElectricSheepAfrica},
year={2026},
license={cc-by-4.0}
}
```

## License

CC BY 4.0 - This is synthetic data generated for research and educational purposes.