AI Agents

Context

Built for banking within the Financial crime risk assessment stack, this agent is used in ongoing surveillance across retail and commercial portfolios. Typical scenarios include monitoring current accounts and cards for emerging risk, refreshing customer risk during periodic KYC reviews, and prioritizing investigations when alert volumes are high.

What it does

The agent analyzes customer and account activity over time and assigns an explainable behavioral risk score. It looks at how cash flows, payments, transfers, and channel usage evolve versus the customer’s own history and relevant peer groups. When behavior deviates in risk-relevant ways - unusual cash structuring patterns, rapid velocity changes, atypical counterparties, or channel shifts - it raises the score and highlights the drivers with links to the underlying events. The output is a current risk rating plus a short rationale that investigators can use immediately.

Core AI functions

Time-series patterning and segmentation establish baselines per customer and peer cohort; anomaly detection surfaces sustained or sudden changes; feature engineering captures velocity, frequency, value distribution, and counterparty mix; rule overlays align with policy; and a transparent reason code framework explains why the score moved (e.g., “cash-in bursts below threshold over n days,” “new counterparties in high-risk geography”). Scores are calibrated and thresholded for your queues.

Problem solved

Traditional static rules produce high volumes of generic alerts and miss context. Teams struggle to see which customers have meaningful behavioral change that warrants review, and why.

Business impact

Investigations focus on higher-value targets, emerging risks are surfaced earlier, and case work starts with a clear explanation of drivers. That improves productivity and consistency while supporting AML objectives with evidence that is easy to review and retain.

Integration and adjacent use cases

Integration is moderate: the agent reads transactional feeds (accounts/cards), customer profiles, and optional device/channel events from your warehouse or streaming bus, then writes scores and reasons into your surveillance or case-management system; no core replacement is required.

It is commonly combined with:

• High-risk MCC detector,

• Cash transaction pattern detector,

• Dormant-to-active spike monitor, and

• Anomalous peer-network growth to enrich the signal and provide complementary, specific detectors alongside the behavioral score.