🛡️ AML AI - Transaction Monitoring

Catch the suspicious -
without drowning the team

An AML monitoring system that generates 95% false-positives does not protect you - it exhausts you. We build monitoring on behavioral analytics and graph, that surfaces the right alerts, explains them, and drafts the suspicious activity report - with an audit trail on every decision.

AML consultation call Back to FinTech AI
⚠️ The real problem

The problem is not generating alerts - it is ranking them

Rule-based systems flood the team with false-positives. Done right, AI ranks risk, explains it, and leaves judgment to a human.

30-50%
fewer false-positives*
Graph
network analysis
SAR
auto-draft
Audit
full trail
⚙️ What we build

End-to-end AML monitoring

📈

Behavioral Analytics

A behavior model per customer - not just fixed thresholds.

  • Peer-group baselining
  • Deviation-from-profile detection
  • Structuring / smurfing detection
  • Dynamic risk scoring
🕸️

Graph & Network Analysis

Money moves in networks - not in single transactions.

  • Community and intermediary detection
  • Money-mule detection
  • Beneficial-ownership traversal
  • Analyst-facing visualization
🛂

Sanctions & Watchlist

Real-time screening against sanctions lists.

  • OFAC, EU, UN and local lists
  • Fuzzy matching for transliteration
  • False-positive reduction
  • Rescreening on list update
📝

SAR / STR Generation

A draft report ready - the human reviews and files.

  • Narrative generation from case data
  • Structure per regulator format
  • Evidence and timeline retention
  • Mandatory human-in-the-loop
🎯

Alert Triage & Scoring

The important alerts on top, with a reason.

  • Risk-ranked queue
  • SHAP explanation per alert
  • Documented auto-close for trivial alerts
  • Team performance metrics
🔌

Integration & Model Control

Wired into core systems, with monitoring of the model itself.

  • Ingestion from core / payments
  • Model registry + drift
  • Controlled threshold tuning
  • Regulator reporting
📋 Regulation

Built for compliance

🇺🇸

US: BSA, FinCEN, OFAC, FATF

Bank Secrecy Act, SAR filing to FinCEN, OFAC screening, 314(a)/(b) information sharing, and FATF principles.

🇪🇺

EU: AMLD + AMLA

EU Anti-Money-Laundering Directives and the new EU AMLA framework, including reporting and record-keeping obligations.

🇮🇱

Israel: AML Order + IMPA

For Israeli institutions: the Prohibition on Money Laundering Order, irregular-activity reporting to IMPA, and Bank of Israel monitoring directives.

📊 Industry benchmarks

What public research shows - and where we come in

📉

Fewer false-positives

What the industry reports: Moving from rule-based to hybrid (behavioral + ML) reduces false-positives by 30-50% while preserving recall (Bain payments tech 2024).

Where we come in: An ML layer on top of the existing rules engine - without replacing it, with controlled tuning.

🕸️

Graph reveals networks

What the industry reports: Network analysis surfaces money-mule and structuring patterns a single-transaction view misses.

Where we come in: Building a graph model on transaction data, with a view an analyst can actually investigate.

📝

SAR automation saves time

What the industry reports: A large share of analyst time goes to writing repetitive narratives by hand.

Where we come in: Drafting the narrative from case data - with a human-in-the-loop before filing.

* Industry benchmark (Bain 2024), not a client metric. SLAtech, since 2004, 14 countries. Commercial examples on a consultation call.

Want AML monitoring that does not drown the team?

30-minute consultation - we review the existing monitoring system and show where AI lowers false-positives without regulatory risk.