Anomaly Detection.
Anomaly Detection helps in identifying unexpected items or events in data sets, which differ from the norm. Credit card companies can use anomaly detection algorithms to identify fraudulent credit card activity within seconds of a transaction taking place. Similarly, early warning signals from IOT sensors makes it possible to schedule maintenance of the machine before the failure occurs, reducing downtime and improving productivity.
Detecting Anomalous
Log-On Patterns
Big data solutions help business organizations to detect and react to both rogue employees and external attacks to the network
Detecting Abnormal
Finance Activities
Detect when employees breach the rules associated with their roles, and also when they are acting in an abnormal way within the rules.
Network Intrusion
Detection
Identify when malware is present on the corporate network and preempt some or all of the damage it could do.
Case Studies.
IoT Sensor Anomaly Detection
IoT sensors attached to factory robots can track variables like vibration, temperature, and machine timing and then feed that information into an analytics platform. The data can then be analysed to predict when a particular machine will need maintenance next.
Fraud Detection in Banking
A typical organization loses an estimated 5% of its yearly revenue to fraud. We apply supervised learning algorithms to detect fraudulent behaviour based upon past fraud and recommend methods to discover new types of fraud activities.