Behavioral intelligence
Build customer profiles that adapt in real time. Detect subtle behavioral shifts, spending anomalies and session irregularities before they become fraud losses.
- Real-time behavioral profiling
- Spending pattern forecasting
- Session anomaly detection
- Drift and deviation alerts
- Event
- TXN 40E8-7B
- Channel
- Online banking
- Amount
- ₦1,850,000
Why it was flagged
- Spending far above this customer's own forecastBehavior
- Login at an hour this customer never usesBehavior
- Navigation unlike this customer's past sessionsBehavior
- Similar customers rarely pay this type of merchantBehavior
The anomaly score and its top drivers go to an analyst with the alert.
- 500+
- Behavioral signals feed each customer profile.
- Live profiles
- Profiles update with every transaction and session.
- Peer comparison
- Customers measured against similar customers.
- Explainable scores
- Each anomaly comes with the behaviors that drove it.
Intelligence that evolves
Static rules miss changing fraud. The behavioral engine keeps learning each customer's patterns to spot deviations the moment they occur.
Pattern evolution tracking
Monitor how customer behavior changes over time with forecasting and change detection.
Spending forecasts
Predict expected transaction amounts and frequencies from historical patterns, and flag significant deviations immediately.
Session fingerprinting
Analyze navigation patterns, timing and interaction dynamics to verify legitimate behavior throughout each session.
Peer group comparison
Compare individual behavior against similar customer segments to identify outliers that deviate from cohort norms.
See what rules miss
Traditional fraud detection reacts to known patterns. Behavioral intelligence anticipates threats by learning what normal looks like for each customer.
- Continuous profile enrichment
- Seasonal pattern awareness
- Velocity and frequency analysis
- Device and location correlation
- Explainable anomaly scores
- Continuous model improvement
How a profile develops
- 1
Baseline
Initial behavior is captured from transactions and sessions.
- 2
Learning
Regular patterns, including seasonal ones, are established.
- 3
Monitoring
New activity is compared with the profile in real time.
- 4
Alerting
A meaningful deviation raises an alert with its drivers.
Questions about behavioral intelligence
What is behavioral intelligence?
Behavioral intelligence uses machine learning to build adaptive customer profiles that learn normal patterns and detect anomalies indicating potential fraud or account compromise.
How do behavioral profiles adapt over time?
Profiles continuously update based on new transactions and interactions, incorporating seasonal patterns, life changes, and evolving customer behavior to maintain accurate baselines.
What is peer group comparison?
Peer group comparison analyzes individual behavior against similar customer segments to identify outliers whose activity deviates significantly from cohort norms.
See what normal looks like for each customer
Discover how adaptive profiling moves your fraud detection from reactive to predictive.