Rank the riskiest digital-channel events first.
MIHZI helps regulated financial and telecom teams test whether behavioural signals can improve fraud/risk investigation prioritisation. We start with one channel, one historical dataset, one success metric, and a controlled diagnostic after data access is agreed. Production SDK/API/console work comes after pilot scope.
Before production, prove the prioritisation works.
MIHZI is pre-product. The first ask is not a full deployment. It is a serious design-partner diagnostic on historical event data, with the work run client-hosted or on a pseudonymised extract.
What we do
We turn event-style data into a ranked investigation queue: the sessions, transactions, claims, or assisted-channel actions most worth reviewing first.
How we ship it
The six-week clock starts after usable data access is agreed. We ingest, engineer behavioural signals, score events, capture analyst feedback, and backtest against current review performance.
What we don't do
We do not ask for raw PII by default, claim production accuracy before client data, or replace fraud teams. The diagnostic supports analysts with reason codes, review queues, and measurable evidence.
What the diagnostic produces, and what it does not overclaim.
The demo and pilot are designed to prove workflow, technical seriousness, and review prioritisation. Accuracy is proven only on the partner's usable historical data.
Where the first design partner is most likely to come from.
USSD/mobile-money telemetry may be hard to access. So the diagnostic is framed for the data each partner already controls: wallet, digital banking, PSP, assisted-channel, claims, or investigation events.
Scope · Access · Diagnose · Recommend.
A diagnostic path for regulated teams that need proof before procurement: define the use case, prepare data access, run the analysis, then decide whether a production pilot is justified.
Founder-built diagnostics,
for regulated risk teams.
MIHZI is led by Brian Musonza, a Kigali-based ML/MLOps engineer with experience in real-time fraud-detection infrastructure, feature stores, production ML systems, APIs, and agentic workflows. The first MIHZI offer is deliberately narrow: prove whether behavioural signals can improve investigation prioritisation before building a larger platform.
How the diagnostic handles regulated data access.
The diagnostic is designed so a partner does not have to throw data over the wall. We can work client-hosted, Rwanda-hosted, or on pseudonymised historical extracts under agreed controls.
Start with one dataset, one channel, one metric.
If you own fraud, risk, digital channels, mobile money, payments, claims, or data platforms, ask for the one-page pilot memo or a 30-minute diagnostic scoping call.