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KYC/AML & Financial Crime

9 pieces from Duczer East's practitioners on the architecture behind financial-crime compliance: sanctions and stablecoin rules read as system requirements, graph-based AML, document evidence, and the data foundations an examiner will test.

What this topic is about

Financial-crime compliance has always been a data problem wearing a legal one. The rules say who must be identified, what must be monitored and what must be reported. The institution's systems decide whether any of that can actually be done — whether beneficial ownership can be resolved across entities, whether a document's provenance survives ingestion, whether a decision made last quarter can be reconstructed with the data that was available at the time.

The pieces in this collection approach KYC/AML from that side. Some take a regulatory event — a sanctions authorization, Treasury's stablecoin rule — and read it as a system specification: which decisions land on the architect's desk rather than the compliance officer's, and where the implementation clock will actually be spent. Others work through the platform question directly: why graph-based entity resolution is becoming the center of AML architecture, what it takes for an AI agent to defend a decision made from a scanned record, and why the next examination finding is likely to be about the meaning of the data rather than the model.

Governance of the agents themselves — identity, permissions, and what SR 26-2 left with the bank when it carved agentic AI out of model-risk guidance — is a topic of its own. That work is collected in Agentic AI Governance & Model Risk.

From insight to delivery

KYC/AML built on data your examiners can trace

Duczer East designs and builds the entity-resolution, monitoring and evidence architecture behind financial-crime compliance — on governed data inside your perimeter, with the audit trail that makes each decision defensible.