Traditional bank-grade AML platforms are alert-centric. They ingest transactions, apply scenarios, generate alerts, and route them into case management. Excel at the layer they were designed for — but the supervisory question is no longer only "did the system generate an alert?"
The new question, embedded throughout the EU AMLR, is fuller: can the institution demonstrate, end to end, why this customer was accepted, how the beneficial owners were verified, what risks were known at each point in time, why alerts were closed or escalated, what evidence supported the SAR/STR decision, and whether governance controls actually function as intended?
DigiDoe Financial Intelligence is built around that the broader question. Every risk signal is linked to verified identity, corporate ownership, source-of-funds and source-of-wealth evidence, transaction behaviour, sanctions exposure, investigator action, decision rationale and retention policy — in one continuously updated, regulator-ready evidence graph.
"Compliance becomes infrastructure for proof — not a queue of alerts to be cleared."
Most institutions run AML, KYC, KYB, screening, fraud, case management and reporting as separate modules — connected by integration, not by design. DigiDoe Financial Intelligence collapses that into a single data, evidence and governance fabric.
Risk-based journeys for individuals, companies, funds, trusts, SPVs and public entities. Document AI extracts identity, ownership and constitutional documents. Expected behaviour, source of funds and source of wealth captured at the start — and reused as live monitoring features.
Native graph for direct and indirect ownership across companies, trusts, funds and nominee structures. AMLR-aligned UBO threshold engine, control-by-other-means logic, and senior managing official fallback. Perpetual KYB monitors directors, shareholders, sanctions and geography.
Continuous screening across customers, UBOs, controllers, signatories and counterparties — with full ownership-and-control sanctions logic. Re-screens on list updates, ownership changes and new transactions, with alert-level explainability.
Scenario, behavioural, graph and real-time monitoring tied to expected behaviour captured at onboarding. Fraud and AML converge in one risk fabric — APP fraud, mule activity, synthetic identity, structuring, pass-through, nesting and corridor anomalies.
Unified queue across AML, sanctions, PEP, fraud, onboarding and KYB. Entity-centric case view, network visualisation, and an AI assistant that summarises facts, identifies missing evidence and drafts notes — with explicit human approval at every decision point.
Jurisdictional templates, narrative assistant grounded in case evidence, evidence-bundle generator, MLRO approval workflow, FIU request management, and built-in tipping-off controls. Post-filing monitoring is automatic.
Policy-as-code, model governance, scenario governance, automated control testing, audit workbench, board-and-MLRO MI dashboards, and outsourcing evidence — designed for AMLR demonstrability from day one.
Established AML, KYC, screening, fraud and case-management platforms are strong, capable products. The differentiation here is architectural, not feature-by-feature.
Designed around regulator-ready evidence from onboarding through FIU reporting — not as attachments retrofitted during investigation.
KYC, KYB, UBOs, expected behaviour and transaction monitoring share one risk model. Onboarding evidence becomes live monitoring features.
Built for layered ownership, SPVs, funds, trusts, family offices and cross-border corporate structures.
Applies financial-crime intelligence at the point of onboarding, account operation and payment execution.
Every AI output is evidence-linked: extraction, scoring, triage and narrative drafting all retain lineage. Human approval is preserved on every consequential decision.
Policies, rules, models, decisions, cases, reports and audit logs are versioned and reconstructable to the moment a decision was made.
APP fraud, identity fraud, mule detection, transaction monitoring and AML typologies converge in a single financial-crime view — not parallel systems with separate lineage.
API-first by design. Modular, white-label, embedded and multi-tenant — aligned with how regulated payments and EMI infrastructure are actually delivered in practice.
Powers DigiDoe's own accounts, payments, FX and treasury services — Financial-crime intelligence at the point of onboarding and payment.
Standalone onboarding, KYB and risk intelligence for fintechs, EMIs and financial institutions — without rebuilding the rest of the back office.
Banks and fintechs call DigiDoe APIs for risk decisions, screening, UBO intelligence and evidence bundles — preserving their own UI and workflow.
Institutions use DigiDoe-branded or client-branded portals for onboarding, monitoring and case workflows.
Sensitive data remains in the client environment while DigiDoe provides models, orchestration, evidence logic and workflow — for institutions with strict data-residency or sovereignty requirements.
Regulation (EU) 2024/1624 applies from 10 July 2027. It raises the bar from "controls existed" to "controls are demonstrably effective and reconstructable." DigiDoe Financial Intelligence is designed around that supervisory model from inception.
Tier-2/3 banks, EMIs and payment institutions modernising their financial-crime stack ahead of AMLR.
Investment funds, fund administrators and asset managers with complex SPV and investor structures.
Single and multi-family offices with multi-jurisdictional structures, trusts and PEP exposure.
Private banks and wealth platforms serving HNWI/UHNWI principals across cross-border structures.
A 60-minute briefing with our Financial Intelligence team. No autoresponders, no procurement gauntlet — direct conversation with the people who built the platform.