Executive reading
This project is built for a GitHub portfolio that must speak to two audiences at once: a doctoral committee interested in development economics and a senior data analytics recruiter interested in reproducibility, risk governance and clear communication.
The analysis links credit-portfolio expansion with financial inclusion, poverty and inequality through cautious proxies: client outreach, territorial balance, credit depth and mora discipline. It does not claim causal poverty reduction without household welfare data.
Key metrics
| Metric | Value |
|---|---|
| Observed period | 2012-03-01 to 2014-07-01 |
| Global portfolio | 109.8 to 69,850.2 thousand BOB |
| Global clients | 18 to 3,827 |
| Maximum global mora | 0.25% |
| Territorial client balance | 0% to 99% |
| Responsible inclusion score | 98.2 / 100 |
| Best global forecast model | ARIMA |
What the project demonstrates
- End-to-end data work: raw Excel ingestion, cleaning, tidy panels, tables, figures, reports and dashboard publication.
- Senior analytics discipline: backtested forecasts, stress scenarios, diagnostic tables and decision-oriented outputs.
- Research discipline: explicit question, conceptual framework, limitations and a path toward causal identification.
- Responsible public reporting: officer-level names remain outside processed public outputs.
Visual evidence
