AML & Compliance Monitoring
Find the networks rules miss, and clear the alert backlog.
An AML monitoring platform that combines graph neural networks, behavioural analytics and risk-based alert prioritisation. Every alert comes with evidence an investigator, or a regulator, can follow.
01 · Overview
Drowning in noise, missing the networks
Rule-based transaction monitoring produces huge volumes of alerts, and most of them are false positives. Meanwhile, sophisticated laundering is spread across many accounts and entities, each staying under every threshold. Compliance teams spend their time closing noise while the real risk sits in the connections between accounts.
Built with
- Graph neural networks
- Sanctions & PEP
- Case management
Deployment
On-premise, private cloud or in-region.
02 · How it works
From transactions to evidence
- 1
Map the network
A graph of customers, accounts, counterparties, devices and corporate relationships. Graph neural networks detect layering, circular flows, fan-in/fan-out and mule structures.
- 2
Profile behaviour
Each customer's activity is compared with their own history, their declared profile and their peer group, surfacing unusual amounts, frequencies, corridors and channels.
- 3
Prioritise what matters
A risk score combines transaction, customer and network signals. Investigators work the highest-risk alerts first; low-risk noise is suppressed with documented rationale.
- 4
Investigate with evidence
Each alert opens a case with the subgraph, timeline and top contributing factors. Investigator outcomes feed back into the models.
- Transactions + KYCPlus watchlists
- Entity resolutionNames, scripts, variants
- Graph & behaviour modelsGNN · anomaly · risk
- Risk-ranked alertsWith reasons
- Case workspaceSAR/STR ↺ feedback
03 · Capabilities
What it covers
- Transaction monitoring across retail, corporate and payment channels
- Sanctions, PEP and adverse-media screening, at onboarding and ongoing
- Entity resolution across names, scripts and transliterations
- Alert triage, case management and audit trail
- Regulatory reporting support (SAR/STR preparation)
04 · Outcomes
What we measure
- Fewer false positives
- Alert-to-case and case-to-SAR conversion rates
- Complex schemes caught
- Network typologies found that rules missed (back-testing)
- Lower cost per investigation
- Analyst hours per alert
- Defensible decisions
- Complete, explainable record for every alert closed
Example scenarios
Illustrative- Retail & corporate bankA network-aware monitoring layer that sits alongside the existing rules system.
- Payment processorCross-border monitoring across multiple corridors and currencies.
- Digital-asset exchangeWallet- and transaction-level risk scoring.
- OnboardingSanctions and PEP screening built into KYC and periodic reviews.
05 · Governance
Explainability & governance
Controls built in
- Every score broken down by contributing factor; network findings shown visually
- Model documentation that supports model-risk-management review
- Full audit trail of alerts, decisions and model versions
- Tuning and threshold changes logged and justified
Technology
- Python
- PyTorch Geometric
- Neo4j
- Kafka
- Spark
- Elasticsearch
- PostgreSQL
06 · FAQ
Common questions
Do we have to replace our current AML system?
No. Most clients start by adding a prioritisation and network-detection layer on top of their existing monitoring.
Can it handle Arabic and Persian names?
Entity resolution and screening are designed for multiple scripts and transliteration variants.
How do we justify suppressing alerts to the regulator?
Every suppressed or down-ranked alert keeps its score, its reasons and the model version that produced it, so it can be reviewed and back-tested.
Make your compliance team's time count.
Start with a discovery session on your alert data and typologies.
Start a project
