Capturing Value with Anomaly Detection - Concord White Paper

Types of Anomaly Detection Problems

TYPE

WHEN TO USE

DETECTION METHOD

EXAMPLE USE CASES

When anomalies are known or well-defined

Labeled inputs, often based on business logic

Fraud detection, bot activity, likely buyer identification

Supervised

Unsupervised- Univariate

Monitoring a single key metric over time

Detects values outside expected range

Transaction volume spikes, unique visitors per minute, unusually high attribute counts Downtime across systems, suspicious behavior based on multiple features, market trends

Use case success depends

on correctly aligning the

Identifies deviations from normal patterns

Unsupervised- Multivariate

Detecting unusual combinations of data points

detection type with your

data context.

Capturing Value with Anomaly Detection