Vigilic is the ML-native AML platform for financial institutions. Behavioral models across 14 risk categories — generating high-confidence investigation cases, not floods of false-positive alerts.
Vigilic's engine doesn't run static rules. It models each institution's customer base, transaction patterns, counterparty relationships, and risk geography — then scores every incoming transaction across hundreds of simultaneous behavioral signals.
What reaches your BSA team is not an alert. It's a case: a ranked, explainable, regulator-ready package of evidence — with supporting network graphs, counterparty context, and typology attribution built in.
For qualified prospects, investors, and institutional partners: a complete engineering-grade walkthrough of each category, the feature set, model governance, and deployment architecture.
If you're a community or regional bank less than $50B in assets, you need the detection capability of the largest institutions without their headcount, budget, or implementation timeline.
Credit unions serve members, not just accounts. Vigilic's per-customer behavioral baselining respects that distinction — flagging anomalies against a member's own history, not arbitrary thresholds.
We don't bolt machine learning on top of rule outputs. Vigilic is an ML-first architecture: behavioral features feeding category-specific models, ensembled into a unified case-generation pipeline with explainability at every layer.
275 features · 14 categoriesPhase 2 unlocks what every single-institution system misses: coordinated laundering networks that deliberately spread activity across banks. Our cryptographic protocol lets institutions collectively detect these networks without exposing customer PII.
No competitor offers this at this tierDisconnected money laundering networks become connected stories. Our network graphs show regulators and examiners the full end-to-end view of the crime — all parties, counterparties, and fund flows from origin through layering to integration. Stronger SARs. More effective law enforcement referrals.
Regulator-ready evidence| Legacy rule-based | Vigilic | |
|---|---|---|
| Detection approach | 15–30 static rules, manually maintained | Behavioral ML, continuously learned |
| Output | Alerts — high volume, low confidence | Cases — ranked, explained, investigation-ready |
| False positive rate | 90%+ industry standard | Target <20% |
| Customer baselining | Population thresholds | Per-customer behavioral models |
| Novel-pattern detection | None — rules must be written first | Anomaly models surface the unknown-unknowns |
| Cross-institution intelligence | None | Secured shared signal (Phase 2) |
| Built for | Enterprise banks with unlimited IT | Community banks & credit unions |
Whether you're a financial institution exploring modern AML, an investor evaluating the space, or a data partner building for financial institutions — we'd like to talk.