Use Cases

Anomaly Detection with AI

Spot and prevent abnormal and costly events in business and operational processes with Artificial Intelligence (AI)

Have you ever tried to find something that you don't really know? Pretty difficult.

Anomaly detection with Artificial Intelligence is about identifying abnormal events, or combinations in business processes or operational processes. You are searching for anomalies, but you don’t know what their characteristics will be.


While identifying abnormal events in large datasets or heavy/complex business processes is hard for humans, this is a field where AI excels.

Artificial Intelligence is indeed the key to spot these abnormal events, leaving humans the responsibility to take action, or not take action.

AI based anomaly detection is usually applied with the following objectives :

  • Detect and prevent anomalies: that can impose a threat or a high cost center;
  • Spend time: only on real anomalies, and disregard low risk situations.


AI models can be :

  • 1- Trained to learn to spot “abnormal behaviors”;
  • 2- Deployed to identify abnormal events quickly and efficiently;
  • 3- Translate anomalies these anomalies in order to enable business users to understand causes of structural defects, errors, or fraud.


Kantify led anomaly detection projects aiming at either reducing or detecting anomalies in operational processes.

These solutions help our clients in the following ways:

  • Understand : Have a granular understanding of sources of anomalies, or patterns leading to anomalies;

  • Optimize: Reduce costs, as anomalies can quickly add up and represent a major cost center;

  • Satisfy: Provide better services to customers as the service or product is of better quality;

  • Motivate: Improve processes and staff motivation, as anomaly detection helps the staff to focus only on value-adding tasks.

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