Continuous compliance intelligence for banks and regulated companies.
Your compliance team sees files. Sentinel sees the whole company.
What actually goes wrong
Every check passes. The story still doesn't add up.
✓ Verified
Certificate of incorporation
Genuine document, valid registry number.
✓ Verified
Director's identity
Passport and liveness check passed.
✓ Verified
Registered address
Matches the submitted documents.
✓ Verified
Sanctions screening
No name match on any list.
↓ read together ↓
⚠ Conflict
Ownership changed 5 weeks ago
The declared owner is not the owner in the registry filing.
⚠ Conflict
Income cannot be evidenced
Declared source of wealth has no supporting financial history.
⚠ Conflict
Payments to unrelated countries
Money moves where the business claims no operations.
Existing tools verify individual facts. Sentinel tests whether the whole story makes sense.
Today's operating model
Compliance runs on labour, not software.
Spent on financial-crime compliance
$206.1B / year, worldwide
Of which roughly $85B in EMEA, with 98% of surveyed institutions reporting rising costs.
LexisNexis Risk Solutions, True Cost of Financial Crime Compliance — Global report · EMEA report
Read documents page by page
Search registries, sanctions lists and open sources
Match names, addresses, ownership, transactions
Rebuild timelines and relationships by hand
Assemble the case so an officer can decide
Banks pay human-intelligence prices for machine-executable work.
Why more people is not the answer
Mechanical work is where the risk is created.
Missed contradictions
A conflict sits in another document, in another system.
Wrong entity matched
Same name, different person or company.
Inconsistent reviews
Two analysts, two conclusions, same file.
Knowledge walks out
Context leaves with the staff who held it.
The cost problem and the security problem are the same problem: critical controls depend on repetitive human work.
What Sentinel does
One evidence model. Every source reconciled.
Don't verify another document. Reconcile the institution with richer signals (device, liveness, social, address) — used only with consent and lawful purpose.
Additional sources & signals
Signals from the customer journey, device and public sources
Liveness & device signals
What the verification step provides
When a user completes ID upload and a liveness check (as with Sumsub/other providers) the journey can surface device and session signals useful for risk analysis:
Device and environment: device model/OS, browser, user agent, camera/microphone availability.
Session signals: IP address, approximate geolocation (with consent), network type and ISP, time-of-day and timezone consistency.
Sensor and media metadata: image EXIF, frame timing, motion pattern (to detect replay/robot), microphone audio fingerprint and speech cadence (with consent).
Verification artifacts: extracted nationality from ID, name parsing, document authenticity scores, face-match confidence and liveness heuristics.
Behavioral & textual analysis
Language, style and provenance
Textual and behavioral signals extracted during onboarding add context to identity claims:
Nationality & ID country cross-checks against declared residence.
Text-style analysis of free-text inputs and any spoken replies: language, register, sentiment, unusual phrasing or script-switching.
Authorship signals: typing cadence, copy/paste patterns, and consistency with declared language.
Where permitted, audio/text are analysed for deception markers and flagged for officer review (subject to policy and consent).
Address & wealth correlation
Go beyond the document image
Addresses are treated as assertions to be reconciled, not simple text fields:
Validate against registries, utility, and postal data to confirm deliverability and historic usage.
Assess source-of-wealth correlation: link addresses to historical transaction locations, corporate filings, and public registers.
Detect nominee or virtual-office clusters: map addresses appearing across unrelated entities to identify likely nominee structures.
Flag addresses with prior association to reported scams, fraud reports, or abuse (open‑source and public complaints).
Social & open-source signals
Search, fetch, analyse
Sentinel searches public social media and open-source traces to augment the evidence graph:
Search by name, email, phone, and ship/registry IDs to find social profiles and mentions.
Extract network signals: associates, company pages, geotagged posts and timestamps.
Analyze content for signs of illicit activity: coordination, solicitations, complaints, scam reports, or anomalous travel patterns.
Summarise risk indicators and link back to the original post or profile so an officer can verify provenance.
These signals are integrated into the evidence graph so officers see not just facts, but provenance and behavioural context — subject to consent and lawful use.
Division of labour
AI does the mechanics. The officer keeps the judgment.
Sentinel
Compliance officer
Collects the evidence
Judges what it means
Extracts the facts
Applies professional judgment
Reconciles the sources
Investigates the ambiguity
Flags the contradictions
Makes the decision
Prepares the case file
Carries the responsibility
Traceable
Every conclusion stays linked to its source.
Human-controlled
No material decision is automated.
Deployed privately
Runs inside the institution's own environment.
Sentinel does not replace the compliance department. It gives qualified officers an AI investigation team.
The economics
From labour-scaled compliance to software-scaled intelligence.
Today
more clients & data → more analysts → more cost and more human risk
With Sentinel
more clients & data → software-scale investigation → expert-controlled decisions
Cost
Less manual work per case
Risk
Fewer missed inconsistencies
Revenue
Faster onboarding decisions
Coverage
More monitored without more headcount
Sentinel enters through an existing labour budget — and becomes the layer the institution reads risk through.
The ask
Build it with us. One workflow, real cases, measurable economics.
Phase 1
Retrospective
Run Sentinel over already-closed cases and compare findings with the original investigation.
Phase 2
Shadow mode
Run alongside the live process without influencing decisions.
Phase 3
Assisted production
Officers work from Sentinel case files; every conclusion approved by a human.
Phase 4
Expansion
More sources, more workflows, continuous monitoring.
We measure
Analyst minutes per case
Share of evidence collected without manual search
Contradictions found per reviewed case
Officer-confirmed relevant findings vs rejected noise
You provide
One document-heavy workflow
Representative historical cases
Compliance subject-matter owner
A technical integration owner
Access to verification logs & consent policy for liveness/device signals (where permitted)