Deep learning detects the subtlest of abnormalities that could signal a breach.
AimAnomaly Detection (AAD), a subset of AimBehaviour, is our proprietary fraud detection module.
Benefits at a glance:
- Out-of-the-box or bespoke solutions help minimise losses from all types of attack
- Detect potential fraud even at the onboarding stage where no profile exists
- Use generic or your proprietary annotated data to flag fraudulent behaviours
- Learn from overall and individual itemised scores for each class as required
- Deployed using standard AimBehaviour web and mobile APIs, no additional integration required
- Capture behaviour across any physical endpoint utilising our web or mobile APIs
- Owned by AimBrain, part of the AimBrain Biometric Identity as-a-Service platform
- Get a continuous, in-session risk-based assessments, across any channel.
Passive, continuous anomaly detection occurs behind the scenes for the duration of the session.
Forming part of our BIDaaS model, behaviours and anomalies can be captured across any device they are using; from a keypad, keyboard, touchpad, mouse or entry system. AAD and its parent module, AimBehaviour, can be deployed in isolation or used in conjunction with our other modules, for additional step-up security when required.
Typical use cases
- In-session tracking whilst in secure enterprise systems
- In-app monitoring for account takeover (both physical and digital)
- Manual fraud reduction at the onboarding stage by using industry or annotated data
- Access or entry to physical location using keypad or touchpad
- To discuss how AimAnomaly Detection (AAD) could flag fraud, from manual and large-scale simulated attacks, contact one of our team for a no-nonsense chat today.