From raw transactions to an explainable, actionable fraud decision — end to end.
Transaction and account data is uploaded (CSV) or streamed in, then validated and prepared for scoring.
A trained classifier scores each account/transaction for fraud probability using behavioral and transactional features.
Every prediction is broken down feature-by-feature — so investigators see exactly why an account was flagged, not just a number.
Accounts are mapped as a network. Community detection surfaces fraud rings and mule-account clusters that isolated scoring would miss.
Risk scores map to concrete actions — silent logging, soft user confirmation, or a hard hold with investigator escalation.
A built-in assistant explains platform features and general banking/fraud concepts in plain language, any time you need it.