Turn behavioural signals into clearer fraud decisions.
MIHZI helps financial-services and telecom teams identify the digital events that deserve attention first. In a focused six-to-nine-week engagement, we turn historical data into a transparent, evidence-led view of risk, designed around your channels, controls, and review capacity. Every recommendation is measured against the outcomes that matter to your team.
Make every investigation count more.
When analysts face more alerts than they can review, the opportunity is not another black box. It is a clearer way to surface the most meaningful signals in data your organisation already controls.
Sharper priorities
We turn event data into a prioritised investigation queue, bringing the sessions, transactions, claims, and assisted-channel actions most worthy of review to the front.
Evidence-led analysis
Across a focused six-to-nine-week engagement, we map behavioural signals, score events, capture analyst feedback, and evaluate findings against your current review process.
Built around your team
MIHZI complements fraud operations with clear reasons, review-ready context, and measurable insight, so your specialists retain control over every decision.
Everything you need to assess a stronger fraud workflow.
MIHZI brings signal discovery, analyst context, and measurable evaluation into one focused engagement. Results are assessed using your data and the operating criteria that matter to your organisation.
Built for the complexity of digital financial services.
MIHZI works with the event data your teams already use, from wallets and digital banking to payments, claims, assisted channels, and investigation workflows.
From question to confident next step.
A clear, controlled path for regulated teams: define the opportunity, agree secure access, uncover the signals, and leave with evidence to guide the next decision.
Built in Kigali for higher-confidence fraud operations.
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. We combine practical machine-learning expertise with a focused approach to help regulated teams make better use of the signals already in their data.
Data access without giving up control.
Your data stays governed by agreed controls. MIHZI can work in your environment, through Rwanda-hosted infrastructure, or with a pseudonymised historical extract, according to the approach that fits your risk and compliance requirements.
See where behavioural signals can improve your review process.
If you lead fraud, risk, digital channels, mobile money, payments, claims, or data platforms, let’s discuss the opportunity in your data and the outcomes your team wants to improve.