Anomaly detection is a data analysis technique that identifies behaviors or events that deviate significantly from the norm. Unlike supervised methods that require a large volume of labeled examples, it relies on a simple principle: the majority of available data reflects “normal” situations, and the goal is to detect cases that deviate from this pattern.

In the business world, anomalies can have very different meanings depending on the context. An unusual financial transaction may indicate a fraud attempt, a series of abnormal clicks may point to a bot on a website, and an unexpected variation in industrial sensors may signal a production defect. In every case, the value lies in having a tool capable of triggering an alert before the consequences become costly.

The strength of this approach is its ability to deal with rare and often unpredictable situations, where a simple predictive model would not be sufficient.

How anomaly detection works

The principle is based on modeling what is considered “normal” and then measuring how far a new record deviates from this model. If the deviation is too large, it is classified as an anomaly.

In practice, anomaly detection algorithms examine the statistical distributions of the available variables. The most common approach is the so-called “Gaussian” approximation, which assumes that most values follow a bell-shaped distribution. The mean indicates the center of the data, and the standard deviation measures their spread. Most values therefore fall within a relatively narrow area around the mean, while observations that lie far outside are interpreted as unusual.

When several variables are involved, the logic applies to each of them. The probability that the observed values belong to the normality zone is calculated for each variable, and then these probabilities are combined. If the overall result is lower than a defined threshold, the record is classified as an anomaly.

It is important to note that this threshold is not chosen at random. Typically, the available data is split into two sets: one to learn what “normal” looks like, and another to test different thresholds and select the one that best separates normal data from known anomalies. Even if anomalies are rare, this validation step is essential to reduce false positives ; that is, normal data mistakenly flagged as anomalies.

In business, this method has the advantage of not requiring a large number of past anomaly examples. It focuses primarily on defining a profile of normality, making it operational even in environments where incident history is limited.

Anomaly detection is now a strategic tool for many industries. It offers a unique ability to identify rare but critical events that are often invisible to a simple predictive model. It can therefore be applied both to performance challenges and to security issues.

While anomaly detection is powerful, it also has limitations. First, it often relies on statistical assumptions such as Gaussian normality, which are not always perfectly met in real-world data. Certain transformations can improve the situation, but they require specialized expertise.

Second, setting the classification threshold is delicate: a threshold that is too strict triggers too many alerts, while one that is too loose allows significant anomalies to slip through. Striking the right balance requires a rigorous validation phase and ongoing adjustments.

Finally, interpreting the results can be a challenge. The algorithm signals that an observation is abnormal but does not always explain why. Companies must therefore establish complementary processes to analyze these alerts and decide what actions to take.