Anomaly Detection
Finding the One Outlying Row Among Thousands
A fraudulent transaction, an illicit consumption, a machine heading for failure — each first appears in the data as a small deviation. The problem is that this deviation is invisible to the human eye among thousands of normal records.
We build anomaly detection systems that learn what normal looks like from the data and flag departures automatically. Instead of fixed threshold rules we model behaviour itself, so previously unseen deviation types can also be caught.
Key Capabilities
Behaviour Profile Learning
A normal behaviour profile is derived for each user, device or subscriber. Using an individual baseline rather than a fixed threshold cuts false alarms.
Real-Time Scoring
A risk score is produced the moment a transaction occurs, with high-risk activity notified instantly or routed automatically for approval.
Multi-Dimensional Analysis
Amount, timing, location, frequency and relationship network are assessed together, catching outliers in combinations where each value looks normal alone.
Explainable Alerts
The system does not merely say "suspicious"; it shows how much each feature contributed, so the reviewer can justify their decision.
Sensitivity Tuning
The balance between missed cases and false alarms is tuned to your operational capacity — calibrated to how many alerts you can genuinely review per day.
Feedback Loop
True and false markings from reviewed alerts feed back into the model, so accuracy improves the more the system is used.
Where It Is Used
- Illicit usage detection in meter data
- Fraud analysis in financial transactions
- Shrinkage and loss tracking in stock movements
- Quality deviation on production lines
- Pre-failure signatures in machine sensor data
- Unusual behaviour in access control logs
- Security breaches in system logs
- Fuel and fleet consumption deviations
How We Work
Data & Case Review
Historical anomaly examples are examined where they exist; where no labelled cases are available an unsupervised approach is planned.
Normal Behaviour Modelling
A normal profile is learned from healthy-period data, accounting for seasonal and cyclical variation.
Threshold Calibration & Pilot
The system runs over historical data and the alerts it produces are reviewed with your team to tune sensitivity.
Go-Live & Improvement
Connected to the live stream, alerts reach operations, and feedback drives periodic model improvement.
False Alarms Are What Kill These Systems
A system producing hundreds of baseless alerts a day gets switched off within weeks. So our goal is not to find the most anomalies but to produce a volume your team can genuinely review, at high precision.