AI Agents

Context

Built for banking within the Financial crime risk assessment stack, this agent is applied at onboarding, periodic refresh, and continuous monitoring to detect when a customer’s counterparty network expands or reorganizes in ways that are inconsistent with their stated profile and peer norms—often an early sign of mule activity, layering, or emerging rings.

What it does

The agent constructs a rolling picture of the customer’s counterparties—who sends/receives funds, through which channels, and at what cadence—and tracks network size, concentration, and connectivity over time. It flags unusual patterns such as sudden bursts of new counterparties, sharp shifts from a few stable relationships to a hub-and-spoke flow, growing reciprocal loops among related accounts, and cross-border edges inconsistent with the footprint on file. Each finding includes a short rationale, confidence, and record-level links to the specific transactions, counterparties, and windows that triggered the alert, so investigators can see what changed, when, and why it matters.

Core AI functions

Counterparty graph construction from ledger and payment rails; time-windowed change detection on node/edge counts, degree and clustering, reciprocity, and concentration; novelty scoring for first-time counterparties and geographies; coherence tests against the declared business profile and peer cohorts; and reason-code generation with line-of-sight to the exact ledger rows and counterparties cited in the alert.

Problem solved

Rapid growth or re-wiring of a counterparty network is a well-known AML risk signal, but manual reviews catch it late or produce noise from benign expansion (e.g., seasonal vendors). Evidence is spread across systems, slowing investigation and yielding uneven narratives.

Business impact

Earlier interdiction with fewer false positives. Emerging rings and mule patterns surface within the review window; benign growth is filtered by profile/peer context; and every assertion in the workpapers carries click-through provenance—accelerating SAR consideration when warranted and improving audit/exam outcomes.

Integration and adjacent use cases

Integration complexity: Medium. The agent reads transaction activity and counterparty identifiers from your core/ledger or AML data store and writes findings, rationales, and evidence links back to onboarding/refresh or investigation workflows; cores remain unchanged.

It is commonly combined within this stack with

• Behavioral risk scoring agent (to translate network findings into portfolio risk tiers),

• High-risk merchant category detector (to reconcile category drift with network changes),

• Cash transaction pattern detector (to separate cash-driven bursts from routine growth), and

• Dormant-to-active spike monitor (to connect reactivation events with rapidly expanding networks).