Syftics understands M-Pesa transaction patterns and local data structures — because generic BI tools were never built for African financial data in the first place.
An AI data analyst purpose-built for African financial data. Syftics understands M-Pesa transaction patterns and local data structures, and surfaces the insights that generic BI tools miss because they were never built for this data in the first place.
Mobile-money statements, till and paybill structures, and agent-network transaction patterns don't map cleanly onto BI tools built around card-network data. Syftics was built around this data from the start.
Understands paybill, till, and agent-float transaction structures directly, without a manual mapping layer that breaks every time a statement format changes.
Time-series anomaly detection tuned to the rhythm of mobile-money flows — flagging the transaction pattern that's actually unusual, not just the largest one.
Ask a question about the data in plain language and get a grounded answer with the underlying transactions attached — not a chart with no way to verify it.
| Data sources | M-Pesa statements and APIs, paybill/till reconciliation feeds, standard bank exports |
| Core capability | Anomaly detection, reconciliation, and natural-language querying over financial data |
| Deployment | Cloud-hosted with data-residency options, or on-premises for regulated clients |
| Integration | API access for embedding results into existing finance and ops tooling |