Pillar · AAE
AI Analytics Engine
The adaptive brain of the network.
Multi-modal machine learning, NLP-driven OSINT analysis, behavioural analytics and graph models that find coordinated fraud campaigns, multi-hop layering and synthetic identities, and turn them into weighted risk scores.
Capabilities
- Multi-modal feature fusion
- Narrative-to-signal NLP for victim reports and chatter
- Behavioural baselines and anomaly detection
- Predictive graph models for mule clusters
- Federated and differentially private training
- Reputation-weighted composite scoring
How it works
- Features are fused from transactions, behaviour, device and text
- Graph models score proximity to known rings
- Outputs combine with CHPE, reputation and validator signals
- Feedback labels recalibrate models continuously
What is novel
- Composite alert scores combine ML, hygiene, reputation and endorsements
- Early-warning prediction from rising chatter
- Privacy-preserving model updates across institutions