AML analysis in minutes, not hours.
Deterministic scoring, full audit trail, human‑in‑the‑loop. Full compliance with regulations (DORA, GDPR, AI Act) and supervisory requirements of KNF, GIIF, EBA and FATF.
Invite-only access. Limited spots available.
Early Access version — indicative results, no SLA.
of AML alerts are false positives
an analyst must spend gathering information and conducting due diligence for a single alert
per month — estimated cost of processing false positives in a mid-size bank
“The problem isn’t a lack of data — it’s the lack of contextual interpretation of that data.”
AML teams in Polish banks face a growing challenge: alert volumes keep rising, but 90–95% are false positives requiring hours of manual due diligence — gathering information from registries, media, and the web, analyzing connections, drawing conclusions. With 2,000 alerts per month and an analyst rate of €23/h, the cost of handling false alarms alone reaches ~€44,000 per month. KNF and DORA pressure is increasing. Team resources are not.
How does Trim Alert AI Assistant work?
Automated due diligence with full audit trail.
- 1 Analyst enters company VAT ID, alert reason, and optionally additional information (e.g. related persons identified by the bank)
- 2 System conducts automated due diligence — analysis of registries, ownership structure, ultimate beneficial owners, and media
- 3 System calculates a deterministic risk assessment with full audit trail
- 4 Analyst receives complete materials for decision-making — risk assessment, key factors, cited sources, and recommendation
Corporate registry analysis
Automated analysis of corporate registries: ownership structure, management board, change history, ultimate beneficial owners. Detection of AML risk patterns in registry data. The system is not dependent on a single registry — it also incorporates information provided by the bank as part of the alert.
Negative media screening
Searching media for negative information about the company and related persons: fraud, criminal proceedings, reputational risks. Private search engine on our infrastructure — queries about your clients never reach external search engines.
Deterministic risk scoring
Algorithmic aggregation of results into a consistent risk assessment. Score calculated deterministically in code — same input always produces the same result. LLM generates only the narrative justification. 5-level scale: LOW → CRITICAL.
The analysis output is a structured digital report containing: risk assessment (5 levels: LOW / MEDIUM / ELEVATED / HIGH / CRITICAL), numeric score (0–100), list of key risk factors with rationale, full audit trail with cited sources, and recommendations for the analyst.
The decision is always made by the AML analyst.
Typical analysis time: < 10 minutes.
Why Trim Alert?
Built for Polish banks
Designed from the ground up based on legal frameworks (GDPR, DORA, AI Act) and supervisory guidelines (KNF, GIIF, EBA, FATF).
Polish jurisdiction, Polish registries, GIIF terminology.
Analyst always decides
The system never makes decisions autonomously — every recommendation requires AML analyst approval.
State machine: REVIEW_PENDING → ACCEPTED / ESCALATED.
Same input, same result
Risk score calculated algorithmically — repeatable, auditable, testable. LLM generates only the narrative.
Validated on a continuously growing set of test scenarios.
Full audit trail
Every recommendation includes cited sources, AI operation identifiers, and a complete audit trail.
KNF auditor sees every operation of each AI Assistant — who, what, when, on what basis.
No vendor lock-in
Migration between LLM models (Azure OpenAI ↔ open source) without system rebuild.
Inference layer abstraction. Private media search engine — queries never reach external search engines.
SaaS and private cloud
Identical agent pipeline — in Azure cloud or in the bank’s infrastructure. One system, two deployment modes.
Configuration via DEPLOYMENT_MODE — no code changes required.
One system, two deployment modes
Try in the cloud or deploy in your bank’s infrastructure.
Both modes run on an identical AI agent pipeline — they differ in deployment configuration, authorization, and data location. Switching from SaaS to Bank Mode doesn’t require rewriting integrations.
Where we are and where we’re heading
Early Access
Automated due diligence — registries, media, risk assessment.
JSON output, SaaS invite-only, max 50 users
Duration: 10–12 wks
Full system
Comprehensive due diligence with sanctions, PEP, OSINT verification and regulatory PDF report.
+ sanctions, PEP, OSINT, PDF report, SSE streaming, commercial plans
Duration: 6–8 wks
Open Source LLM
Your own AI model, full control over processing.
vLLM on Azure, Confidential Computing
Duration: 4–6 wks
Bank Mode
Deployment in the bank’s infrastructure, full data control.
Keycloak + AD/LDAP + MFA, on-premise, full RBAC, dedicated SLA
Duration: 4–6 wks
Estimated timelines. Phase 2–4 timelines depend on Early Access results and deployment schedule.
Security and compliance — designed in, not bolted on.
In the regulated banking sector, compliance isn’t optional — it’s a requirement. A system compliant with regulations (GDPR, DORA, AI Act) and supervisory requirements (KNF, GIIF, EBA, FATF) from day one is a system that can be deployed without legal or reputational risk.
Security — your CISO will approve this: Banking-grade encryption, private search engine (queries never leave our infrastructure), full audit trail of every operation, DORA and GDPR compliance, ICT supplier card available on request. Data never leaves the European Economic Area.
Pricing
Transparent pricing tailored to your institution’s scale.
Starter
PLN 14.95 / alert
20 alerts / month
Small institutions, pilots
Professional
PLN 11.99 / alert
100 alerts / month
Small commercial banks, foreign branches
Business
PLN 10.00 / alert
300 alerts / month
Mid-size commercial banks, associating banks
Enterprise
custom pricing
Unlimited alerts
Top 10 banks, on-premise, SLA
Need on-premise deployment or a dedicated SLA?
Book a call →Prices in PLN, net. All plans include the full AI pipeline.
Enterprise / Bank Mode: custom pricing, contact via form.
From first meeting to production in 5 steps
You can opt out at any step. No commitment until step 3.
Discovery
NO COMMITMENTWe learn your needs, demo on public data.
Result: Fit report; go/no-go
Pilot
NO COMMITMENTSystem works on a sample of your alerts. Comparison with manual process.
Result: Quality results vs AML analyst
Expansion
Full analysis scope, shadow mode alongside analyst.
Result: Comparative report: system vs analyst
Calibration
We fine-tune thresholds to your requirements.
Result: Metrics: time reduction, precision
Production
Full deployment, team training.
Result: Full operation + evaluation report
Want to discuss deployment in your bank? Book a 30-minute call — no commitment.
Book a call →We respond within one business day. Contact: contact@trimalert.com
How do we validate system quality?
We validate quality on a set of 50 AML scenarios — from obvious false positives to complex straw-man structures and high-risk jurisdiction cases. Every system result is compared with an AML expert’s assessment.
test scenarios
True Positive alerts
False Positive alerts
agreement with expert assessment
risk score tolerance
The system handles challenging cases:
- Company not in registry
- Suspicious ownership structures
- Connections to high-risk jurisdictions
- Partial results (one data source failure)
- Single-person ownership structure
The golden dataset is an internal test set used to validate the pipeline before launch. Golden dataset results do not guarantee production outcomes for all company types and alerts.
A startup that thinks like a bank
40+ years of combined experience in compliance, banking IT, and regulation. That’s why our solution is mature and thoughtful — it addresses real AML analyst needs, not assumptions about them.
We know that AML needs aren’t easy to translate into software — we can do it because we know them from the inside.
- Translates compliance requirements into modern software
- 20+ years in IT, Data & AI in banking and insurance
- Product architecture, AI pipeline, tech strategy
- Technology Advisory Manager at Elitmind
- Ex-CTO at DataScience House, Ex-Banking Data Manager at Santander CIB
- AML expert — translates regulations into technical requirements
- 20+ years in AML, compliance & financial crime prevention
- Personally underwent GIIF and KNF audits
- AML Lead at KIR — STIR, SINF, SCU AML systems
- Ex-AML Lead at PKO BP — 3rd AML Directive implementation
Frequently asked questions
Product
What is Trim Alert AI Assistant?
What data does the system analyze?
How long does analyzing one company take?
Does the system replace the AML analyst?
What is deterministic scoring?
What companies can I analyze?
What if I disagree with the risk assessment?
How do you differ from existing screening systems?
Security & compliance
Where is data stored?
Is the system DORA compliant?
How is personal data (PESEL) protected?
Can I run the system in my bank’s infrastructure?
What does the audit trail look like?
What happens to data after contract termination?
Commercial
How much does it cost?
Is there a trial version?
How does the bank pilot work?
What is the SLA?
How to transition from EA to commercial?
Does a retry count toward the alert limit?
SaaS vs Enterprise
What’s the difference between SaaS and Bank Mode?
Can I start with SaaS and switch to Bank Mode?
Where is data processed in SaaS mode?
When will Bank Mode be available?
What’s needed to start a pilot?
Join Early Access
Be among the first users. Invite-only, limited to 50 spots.
Let’s talk
Have a question about deployment, bank pilot, or a demo? Write to us.
You can also write directly: contact@trimalert.com