Anomaly Detection
Detecting anomalies to prevent disruptions
An AI algorithm for Anomaly Detection is trained to capture objects in the dataset that deviate from the norm and investigate possible correlations with other anomalies. Unusual data can indicate critical incidents, such as a technical failure or a fraud attempt, or potential opportunities, such as a change in consumer behavior. Integrated into analytical and production tools, anomaly detection helps your company forge its path to the future.
Technology
Ensemble Learning
The most innovative Anomaly Detection is based on the use of various types of Machine and Deep Learning algorithms in sync with each other.
Unsupervised Models
At the core of the most advanced algorithms are unsupervised or partially supervised models: they learn to recognize anomalies starting from unclassified data.
Clustering Techniques
From the K-means algorithm to DBSCAN, clustering tools allow the detection of data that deviates from the rest in a multidimensional space.
Use cases