AML Risk & Investigation Intelligence

DataWalk is a platform that connects previously siloed data across an organization's AML program, including fraud, customer service, and external registries, to help investigators find unknown activity, speed up investigations, and meet risk-based regulatory requirements. It resolves real-world entities such as people, accounts, and transactions, and reveals the relationships between them, so analysts see networks and layering that individual controls review in isolation.

DataWalk brings together internal and external AML-relevant data that would otherwise stay siloed, including KYC/CDD records, transaction monitoring, SAR and case history, payments and wires, correspondent banking data, sanctions screening hits, trade and shipping documents, and crypto analytics. It organizes this data to reflect real operations by resolving real-world objects, such as people, accounts, and devices, and revealing the relationships between them, rather than leaving the information in separate rows and columns.

Individual AML controls, such as transaction monitoring or sanctions screening, evaluate each signal on its own, so an event can pass, stay in profile, or show no match even when it is part of a larger scheme. DataWalk connects signals across accounts, transactions, and relationships into one view, letting investigators detect networks, layering, and suspicious patterns that no single control can see, and adapt as typologies and tactics change.

Yes, DataWalk can run air-gapped, on-premises, or in your private cloud, keeping the deployment location separate from who governs the system. Your team owns the ontology, the rules, and every change, without a vendor engineer involved in operating it. DataWalk uses open formats and standard interfaces, so an organization's data and model can be moved elsewhere whenever the organization decides to do so.

DataWalk supports investigation of four AML typologies described on its program page: mule networks, complex layering, beneficial ownership, and perpetual KYC. For each, DataWalk lets investigators look across the full population rather than one account at a time.

• Mule Networks: shared beneficiaries, devices, or flow-of-funds patterns across related accounts
• Complex Layering: rapid movement, circular flows, and chains of related counterparties
• Beneficial Ownership: multi-layer corporate structures and indirect ownership traced without manual calculation
• Perpetual KYC: new counterparties, ownership changes, and previously unknown associations after onboarding

DataWalk gives AI agents governed enterprise knowledge and graph computation to work through step by step, so an agent can inspect a result, then narrow, branch, or change direction rather than following one fixed path. This approach addresses a data quality concern: if the underlying data is fragmented, inconsistent, or incomplete, AI automates the same uncertainty investigators already face, so DataWalk connects and resolves that data before agents work with it.