How-To: Execute Anomaly Detection at Scale in 5 Easy Steps

Use Cases & Projects, Scaling AI Nancy Koleva

Anomaly detection is an approach that can be useful across an array of industries and for a variety of purposes. But the underlying, unifying factor is the ability to detect small changes or differences in a system that might otherwise go unnoticed. Uncovering anomalies using machine learning allows humans (or other systems) to take action based on these outliers.

The ultimate goal of anomaly detection is the value of the insight that outliers provide. That is, for a business, money saved from preventing equipment damage, money lost on fraudulent transactions, etc.

  • In banking, it can be used for early detection of insider trading, among many other use cases;
  • In retail, it can be applied to quality assurance and minimizing faulty item production;
  • In health care, it can mean earlier detection or easier treatment;
  • And so much more!

Anomaly detection has a large variety of real-world applications, but how do you get started? Watch this video to understand the basics and learn the 5 key steps to implementing anomaly detection at scale (in just one minute!):

Go Further:
Fraud and Anomaly Detection in Banking Guidebook

Anomaly detection is a challenge, but the operationalization of anomaly and fraud detection models is critical for the banking sector. Build an ML-powered fraud detection system today by leveraging the code samples and tips included in our new Fraud and Anomaly Detection in Banking Guidebook.

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