AI-Driven Business with Anomaly Detection

Anomaly detection is a technique used in a wide range of sectors and applications, including IT and DevOps, manufacturing, healthcare, banking and finance, and government as well.

Finding patterns of interest (outliers, exceptions, oddities, and so on) may vary from expected behaviour within a datasets is what anomaly detection is all about. It’s worth noting that, under this concept, anomaly detection is quite similar to noise reduction and novelty detection. Though anomaly detection detects patterns that are of interest, noise detection is a slightly different because the only objective of detection is to remove abnormalities – or noise – from data.
A working model or algorithm is not the only final aim or outcome of anomaly detection. Instead, it’s about the value of the information provided by outliers which is cutting cost for a business by preventing the equipment from damaged, money lost on fraudulent transactions, and so on. In health care industry, it can mean earlier detection or easier treatment.

Why Anomaly Detection?

Anomaly detection is a technique that may be used in a wide range of industries and applications. The capacity to identify minor changes or variations in a system that might otherwise go unnoticed is the underlying, unifying component. Anomalies can be discovered via machine learning, allowing people (or other systems) to act on them.
RDA Data Analytics solutions provide a non-exhaustive list for anomaly detection systems that includes the following: