Design Dimension
Legacy Bank-Grade AML Pattern
DigiDoe Financial Intelligence
Operating model
AML suite integrated into bank systems.
Compliance intelligence embedded directly into regulated payments, onboarding and account operations.
Primary object
Alert, case, customer, transaction.
Evidence graph connecting customer, entity, UBO, account, transaction, counterparty, risk event, decision and report.
Onboarding-to-monitoring link
KYC and transaction monitoring connected — but operationally separate.
Expected behaviour, ownership, source of funds and payment purpose become live monitoring features from day one.
Complex ownership
Handled through KYC/CLM integrations and manual enrichment.
Native graph model for layered UBOs, SPVs, trusts, nominees, public entities, funds and cross-border groups.
Evidence handling
Audit logs and case attachments exist, but evidence is fragmented across systems.
Evidence Vault is a core product layer: every decision, document, data source, model version and investigator action is retained with lineage.
AI role
Alert scoring, anomaly detection, investigation support, model tuning.
AI used across onboarding, ownership extraction, risk summarisation, alert triage, SAR/STR drafting, policy testing and governance — with human approval and evidence linking on every output.
Regulatory readiness
Strong but often implementation-heavy.
Designed around AMLR-style demonstrability, retention, outsourcing governance, FIU response and policy-as-code from inception.
Deployment
Enterprise implementation, often with complex data programmes.
API-first, modular, white-label, embedded and multi-tenant by design — aligned with how regulated payments are actually delivered.