Anomaly Detection

Identify & predict abnormal patterns in unbounded data streams.

anomaly detection

Anomaly Detection

Point Anamolies

A single instance of data is considered an anomaly, if it's too far off from the rest.
E.g.: Detecting credit card fraud based on the "amount spent" &/or the "transaction frequency"

Contextual Anomalies

This type of anomaly is more common in time-series data, where the abnormality is specific to the context.
E.g.: Spending $100 on food each day is considered normal during a holiday season, but may be considered odd otherwise.

Collective Anomalies

This is a type of anomaly where a set of data instances collectively help in detecting anomalies.
E.g.: An act of copying data from a remote machine to a local host unexpectedly, is anomaly that may be flagged as a potential cyberattack or data leakage.

Target Industries


IT & DevOps

Intrusion detection (system security, malware, etc.), monitor production systems and network traffic surges & drops, etc.


Banking & Insurance

Real time fraud detection (cards, insurance, etc.), stock market analysis, early detection &/or prediction of insider trading, etc.



Predictive maintenance, proactive product & process enhancements, service fraud and fault prediction & detection, etc.



Condition monitoring, early detection of seizures, tumors, diabetes, etc. and real-time knowledge sharing.



Detect network intrusions, prevent equipment failures, predict & prevent fraud, proactive quality enhancements, etc.


Retail & e-Commerce

Proactive detection and prevention of revenue leakages — e.g. monitor & detect anomalies in product pricing &/or quantities listed on the website, etc.

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