AI Agents

Context

Built for banking within the Financial crime risk assessment stack, this agent is used at onboarding and periodic review to verify a merchant’s true line of business and surface prohibited or restricted categories before exposure accumulates.

What it does

The agent checks declared category information against evidence. It reviews KYB/onboarding data and assigned MCCs alongside recent acquiring activity to confirm category accuracy and detect drift into higher-risk segments. It returns a merchant-level finding with a short rationale and direct evidence links, so reviewers see exactly what was inferred and why.

Core AI functions

Normalization of KYB/MCC fields; text understanding over merchant descriptors; activity-pattern analysis on acquiring data (e.g., ticket mix, refund/chargeback behavior, cross-border share) to infer category; drift detection across review windows; and reason-code generation with line-of-sight to the underlying records.

Problem solved

Category mislabeling or silent drift raises AML/sanctions and scheme-fine risk and inflates chargebacks; manual checks are inconsistent and typically reactive.

Business impact

Earlier interdiction and cleaner audits. High-risk categories are identified and evidenced up front, remediation is precise, and supervisory findings and rework are reduced.

Integration and adjacent use cases

Integration complexity: Medium. The agent reads merchant KYB/MCC data and acquiring/chargeback records from your onboarding or merchant-risk systems and writes flags, rationales, and evidence links back to the same workflow; no core replacement required.

Common combinations in this stack:

• Behavioral risk scoring agent (portfolio-level risk tiers),

• Cash transaction pattern detector (cash-heavy behaviors),

• Dormant-to-active spike monitor (sudden volume changes), and

• Anomalous peer network growth (emerging rings and related-party signals).