Our goal is simple: make fraud intelligence more usable, more structured, and more relevant to the realities of African financial ecosystems. FraudSense brings together machine learning, graph analysis, behavioral detection, community intelligence, and a Zambia-focused fraud technique matrix into one operating surface.
What visitors can see right now
- The public fraud technique matrix at fraudsenselabs.com/research
- Community lookup and reporting tools for public-interest fraud intelligence
- A live HTTPS deployment on the FraudSense Labs domain
- An operating backend with database, cache, graph store, and model bundle connected
Why the research layer matters
Fraud reporting often stops at symptoms: a scam number, a suspicious transfer, a known mule account. We want to map those symptoms to repeatable techniques and tactics. That is why the FraudSense matrix uses structured `FZ-TXXX` identifiers to document how fraud works, what signals can detect it, and where coverage is still missing.
What comes next
The next phase is turning this foundation into a stronger public research engine and a more refined operations product. That includes ongoing blog publishing, public advisories, technique writeups, and tighter workflows for institutional users.