Trade Surveillance for the Age of AI

Built on extensive customer feedback and decades of experience

Polaris was designed from a clean sheet, informed by in-depth interviews with 50+ institutional customers on the persistent failures of legacy trade surveillance. The patterns recurred across the cohort — each a problem the industry has lived with for decades, each addressed by a deliberate design choice in Polaris.

Broadest asset coverage — crypto and prediction markets included

Same platform, same data layer, same detection engine across the full taxonomy of regulated trading activity. Extending to a new asset class is a configuration exercise, not a re-platforming engagement — one platform, one analyst console, one model-risk framework across the firm.

Mapped from regulation to detection — for defensibility

The Behavox Unified Risk Taxonomy (URT) is maintained by a dedicated in-house team of regulatory researchers, market-abuse analysts and detection engineers, mapping regulatory regimes to specific risk policies and situations. Audit-defensible coverage at deployment: your engineers don't research regulation — Behavox does. Every policy is version-controlled, attributed and dated, and carries its regime citation, definition, parameters and validation evidence.

Three detection methods. One platform.

A taxonomy is only as good as the detection behind it. Higher recall. Lower false-positive rate. One model-risk framework. Rules, statistics and machine learning run as first-class peers — each catches what the others miss. Behavox AI sits downstream as an optional filter.

Alerts route through your L1 · L2 · L3 investigation tiers as a single auditable case file per coordinated episode, with an end-to-end audit trail. Cross-product detection links instruments across three layers — declared reference data, derived benchmark/structural relationships, and behaviourally discovered correlations — so coordinated episodes that span asset classes are caught, not dropped.

State-of-the-art AI for false-positive suppression

Detection generates the alerts; AI keeps the queue manageable. Behavox AI brings frontier-model reasoning to every alert — inside your tenant, under full model governance, with a human reviewer on every compliance decision. Customers see up to a 95% reduction in alert volumes. You choose the configuration; detection coverage and the audit trail are identical across all three.

No autonomous compliance decisions. Third-party model providers, where used, are consumed via enterprise private endpoints with zero data retention; alert content is processed inside your tenant.

Behavox Scout — a mini-Quant for every reviewer

Once an alert lands, the investigator needs answers fast. Plain-English queries. Read-only by design. Audit-grade evidence packets attached to every response. Investigators answer their own research questions in minutes, not hours — Cmd+K from anywhere, and Scout plans, executes and returns the evidence (it cannot mutate state, alter alerts or change rules).

AI you can defend to your model-risk committee

• Configuration is AI-assisted — Wizard (generate a config from a plain-English goal) · Tune (refine from a complaint) · Explain (plain-English on any parameter) · What-If (replay candidates against historical data).
• Scenario Testing Lab (STL) — test new or modified logic against real production data, side-by-side with the live model, with no live alerts generated. Validation reports fold into the model-risk package.
• Model Validation Portal — feature contributions per alert (SHAP), version history with diffs, training-data summary, performance by method, drift alerts. Vendor updates require client-side STL validation; you can freeze versions during a regulatory examination.
• EU AI Act — Polaris carries a high-risk classification, with conformity documentation aligned to Articles 9, 11 and 14.

Trade Reconstruction

Complete, regulatory-formatted trade timelines in under 72 hours — vs. multiple business days on legacy platforms. Initiated from within an alert or Scout search; formatted for regulatory submission including FINRA, SEC, CFTC, FCA, MAR, ESMA; immutable post-generation.

AI-assisted integration — months become weeks

AI proposes the field-by-field mapping from your source systems into the canonical surveillance data model; your data engineer accepts, refines or overrides line-by-line. Production-quality streams in 4–8 weeks — and the same methodology maps your existing detection models (rules, thresholds, classifiers) from the incumbent system into Polaris, with historical alerts and training data where the legacy platform permits export. Market data is included — LSEG/Refinitiv plus specialty providers for prediction markets, crypto venues, on-chain oracles and energy benchmarks — removed from your scope entirely.