Behaviour is the signal that static rules miss
DetectX® applies machine learning to patterns and anomalies in user behaviour, across devices and across platforms, to detect fraud, money laundering and cyber threats. Detection is fully automated and the model keeps moving as behaviour does.
- 1Observe
- 2Detect
- 3Review
The DetectX® Approach
Behavioural Analysis is one engine of the DetectX® platform rather than a separate product. It shares the AlertViewer, the audit history and the reporting with every other module, so what it detects arrives where the rest of the work already happens.
Behaviour, across channels
The engine tracks user behaviour across devices and platforms, providing a defence beyond traditional security measures. It reads the interaction itself rather than the credential that opened it.
Devices and platforms
Cross-device
Behaviour is followed across the channels a customer actually uses, not assessed one channel at a time.
How, not just what
Interaction
The engine reads the shape of the activity, so a session that passes authentication and still does not fit is visible.
Large data, in real time
Volume
Vast amounts of data are analysed in real time, recognising patterns and anomalies indicative of fraudulent activity.
Machine learning that keeps moving
Detection is fully automated and ML-driven. The models continuously evolve with new patterns, so protection does not depend on a rule someone remembered to write.
Evolves with the threat
Adaptive
Continuously evolves with new fraud patterns, so the organisation stays protected against the latest threats.
Adapts as behaviour shifts
Instant
Recognises and adapts to evolving behaviours instantly, maintaining protection as typologies change.
No manual retuning
Automated
Fully automated, ML-driven detection of fraud, money laundering and cyber threats through behavioural patterns.
Into the same queue
What the engine detects opens as an alert in the AlertViewer, with configurable workflows for triage, escalation, documentation and audit-proof case management.
Alert management
Queue
Behavioural alerts sit alongside screening and monitoring alerts in one queue, prioritised together.
Link analysis on demand
Context
Connections between entities can be mapped from inside the alert when the behaviour needs a wider view.
Audit-proof case management
Record
What was detected, what was decided and who decided it are recorded together.
Key Benefits and Impact
A rule catches the typology you already know about.
Static rules encode last year's fraud. Behavioural Analysis is ML-driven and continuously evolving, so it recognises patterns and anomalies that were never written down, tracks them across devices and platforms rather than one channel at a time, and hands the analyst an alert in the same queue as everything else.
WHAT A BEHAVIOURAL ALERT CARRIES
Why DetectX®
Detection that only knows the old pattern finds the old fraud.
One behavioural alert, as the record holds it
Behavioural Analysis works with Digital Identity, Pattern Recognition, Link Analysis and Customer Risk Score on the same analytics core. Adding a second capability is a module, not a second system, and never a second review workflow.
Step 1 of 4 · Observe
The engine reads behaviour, not credentials
Activity is captured across devices and platforms, so the picture is of the person acting rather than of one session on one channel.
Recorded: what was captured, on which channel, and when.
Step 2 of 4 · Detect
Automated, and continuously retrained
Detection is fully automated and ML-driven, and the models evolve with new patterns rather than waiting for a rule change.
Recorded: what the model flagged, and the signals behind it.
Step 3 of 4 · Review
The alert opens with its evidence
The alert arrives in the same queue as screening and monitoring alerts, with the behaviour that produced it attached and link analysis available from inside it.
Recorded: who opened the alert, and what they looked at.
Step 4 of 4 · Decide
The workflow is the record
The decision is documented through the same configurable workflow the alert was triaged and escalated in, so the triage, the escalation and the close are one audit-proof case rather than three notes about it.
Recorded: the decision, the workflow it followed, who closed it and when.
Common questions
Fraud, money laundering and cyber threats, through patterns and anomalies in behaviour. Detection is fully automated and machine-learning driven rather than rule-authored.
A rule encodes a typology someone has already described. The behavioural models continuously evolve with new patterns and adapt to changing behaviour, so they reach activity no rule describes. The two run together on the same platform.
Yes. Behaviour is tracked across devices and platforms rather than assessed one channel at a time.
Into the same AlertViewer as screening and monitoring alerts, with configurable workflows for triage, escalation, documentation and audit-proof case management.
Behavioural Analysis · DetectX®
See behaviour flagged,
worked and closed.
Book a session on your own channels. Bring your questions about what the models read, how they are retrained and how a behavioural alert reaches the queue.
- Fully automated
- ML-driven detection of fraud, money laundering and cyber threats through behavioural patterns.
- Across channels
- Behaviour tracked across devices and platforms, not one channel at a time.
- One platform
- Shares the AlertViewer, the workflows and the audit history with every other module.