Primary Dataset
Secondary Dataset
Join Key
Synchronization Workflow
A2F 2023
financial behaviour
financial behaviour
+
Climate Data
flood / climate risk
flood / climate risk
→
Harmonization Engine
match by state/LGA
match by state/LGA
→
Combined Intelligence
dashboard/API/sandbox
dashboard/API/sandbox
Example Combined Insight
A2F 2023 financial inclusion data synchronized with Climate Risk data shows that flood-prone states
with lower female inclusion rates represent high-priority opportunity zones for FSP intervention.
Synchronization Output Preview
| State | A2F Inclusion Rate | Climate Risk | Combined Opportunity Score | Recommended FSP Action |
|---|---|---|---|---|
| BORNO | 11.13% | Simulated | 94 | Agent banking expansion |
| SOKOTO | 15.67% | Simulated | 89 | Agent banking expansion |
| KEBBI | 20.68% | Simulated | 84 | Agent banking expansion |
| TARABA | 25.38% | Simulated | 80 | Mobile money activation |
| EBONYI | 26.66% | Simulated | 78 | Mobile money activation |
| BAUCHI | 29.02% | Simulated | 76 | Mobile money activation |
| KANO | 29.16% | Simulated | 76 | Mobile money activation |
| JIGAWA | 29.30% | Simulated | 76 | Mobile money activation |
| ZAMFARA | 29.90% | Simulated | 75 | Mobile money activation |
| KATSINA | 29.95% | Simulated | 75 | Mobile money activation |
Why Synchronization Matters
Financial inclusion datasets rarely exist in isolation. The synchronization layer enables EFInA datasets to be linked with climate, geospatial, demographic, regulatory and FSP operational datasets through common join keys such as State, LGA, Settlement or Geospatial Coordinates. This allows stakeholders to generate richer insights without manually restructuring source datasets.