Predictive analytics for financial inclusion in Malawi using World Bank Global Findex 2024 data.
# Financial Inclusion in Malawi: Global Findex 2024 Analysis
**Author:** Brian Thuwala
**Date:** 2025–2026
**Python:** 3.11+ | **Data:** World Bank Global Findex 2024
---
## Overview
This project analyzes financial inclusion in Malawi using the World Bank's
**Global Findex 2024** nationally representative microdata (n ≈ 1,000 adults).
Four Jupyter notebooks progress from data validation through descriptive
statistics to survey-weighted logistic regression and evidence-based policy
recommendations.
### Key Findings
| Indicator | Value |
|---|---|
| Any account ownership | ~50% |
| Mobile money only | ~35% |
| Formal bank account | ~12% |
| Gender gap (M − F, formal) | ~6 pp |
| Income gap (Q5 − Q1) | ~37 pp |
| Top barrier | Lack of money (~36%) |
> Mobile money is Malawi's dominant inclusion channel. Traditional banking
> reaches only 1 in 8 adults.
---
## Project Structure
```
findex-malawi-analysis/
├── README.md
├── requirements.txt
├── DATA_DICTIONARY.md
├── data/
│ ├── raw/
│ │ ├── Findex_Microdata_2025_updateMalawi.csv
│ │ └── codebook_microdata_2025.pdf
│ └── processed/
├── notebooks/
│ ├── 00_executive_summary.ipynb
│ ├── 01_data_import_and_checks.ipynb
│ ├── 02_descriptive_analysis.ipynb
│ ├── 03_barriers_analysis.ipynb
│ └── 04_from_barriers_to_policy.ipynb
├── dashboard/ ← ✨ NEW: Interactive web dashboard
│ ├── app.py
│ ├── utils.py
│ ├── assets/
│ │ └── style.css
│ └── README.md
└── outputs/
├── figures/ ← saved plots (PNG, 200 dpi)
└── tables/ ← exported CSVs
```
## Notebooks
| # | Notebook | Purpose |
|---|---|---|
| 00 | Executive Summary | Policymaker-friendly overview of key findings and recommendations |
| 01 | Data Import & Checks | Load raw CSV, validate structure, document variables |
| 02 | Descriptive Analysis | Weighted national indicators, disaggregation by gender / residence / income / education |
| 03 | Barriers Analysis | Mobile money barrier prevalence (fin14a–e), demographi …