Banking & Financial Services
Real-Time Fraud Detection
Behavioural signals and rules combined into a scored decision, with analyst review built into the same workspace.
The problem
- Rule-only engines generate alert volumes analysts cannot clear
- Investigation context lives across three or four separate systems
- Feedback from analyst decisions rarely reaches the model
How Mimasa solves it
Layered Scoring
Deterministic rules and behavioural models score each transaction together, so obvious cases never reach a queue.
Case Workspace
Transaction history, device signals and prior cases are assembled into a single investigation view.
Closed Feedback Loop
Analyst dispositions feed straight back into scoring thresholds and model retraining.
The workflow
- 1Stream transactions and enrich with customer and device context
- 2Score against rules and behavioural models
- 3Auto-clear low risk, hold high risk, queue the middle
- 4Investigate in a case workspace with full context
- 5Feed dispositions back into thresholds and models
Outcomes
Lower noise
Fewer false-positive alerts
Faster triage
Context assembled automatically
Continuous learning
Decisions improve scoring
Want this running on your data?
Talk to the Mimasa team about a scoped pilot on your own systems.
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