AI Agents

Context

Built for banking within the Financial crime risk assessment stack, this agent is applied at onboarding and periodic review to assess whether a customer’s cash activity profile aligns with declarations, peer norms, and policy. It focuses on cash-heavy behaviors that elevate AML exposure and require timely, evidence-backed review.

What it does

The agent ingests deposits, withdrawals, and cash-equivalent movements across accounts and review windows, then tests for structuring/smurfing sequences (just-under thresholds, staircase patterns), unusual cash intensity versus the stated business profile, round-amount clustering, rapid velocity shifts following dormancy or onboarding, and location/terminal outliers inconsistent with the customer’s footprint. Each finding includes a short rationale, confidence, and record-level links to the specific transactions and time buckets that triggered it, so investigators see what happened, when, and why it matters.

Core AI functions

Normalization and periodization of cash transactions; windowed threshold and cadence tests for structuring; burst/change-point detection for cash velocity; peer/profile coherence checks; branch/ATM geolocation outlier detection; and reason-code generation with line-of-sight to the underlying ledger rows and channels.

Problem solved

Manual spot checks produce inconsistent cash-risk judgments—missing subtle structuring while over-flagging benign spikes. Evidence scattered across accounts and systems slows investigations and weakens narratives.

Business impact

Cash-risk reviews become timely, consistent, and AML-ready: structuring sequences are surfaced within the review window for on-time SAR consideration; CTR-relevant activity is distinguished from non-reportable bursts to cut false positives; policy thresholds (amount, frequency, location) are applied uniformly across customers; and every assertion in the workpapers has record-level provenance that drops straight into SAR narratives and audit exams.

Integration and adjacent use cases

Integration complexity: Medium. The agent reads account activity from your core/ledger or AML data store and writes flags, rationales, and evidence links back to the onboarding/refresh or investigation workflow; core systems remain unchanged.

Common combinations in this stack:

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

• High-risk merchant category detector (to align cash expectations with true merchant type),

• Dormant-to-active spike monitor (to catch reactivation with cash bursts), and

• Anomalous peer network growth (to expose emerging rings and related-party flows).