Anomaly Detection Model

The ability to detect outliers in a massive pool of data has numerous practical applications, from detecting fraudulent activity to bolstering cybersecurity. It’s no wonder then why anomaly detection algorithms are one of the most popular use cases for machine learning today.

How do anomalies work, and how do businesses apply machine learning to this field for this use case?

What Is an Anomaly?

Significant amounts of data typically follow a general trend, whether it’s a bell curve, a trend line, or any other pattern. In the real world, however, any data point that deviates from the norm is a rare occurrence known as an anomaly.

For instance, a cybersecurity anomaly can take the form of a new user signing into a network or a sudden burst of activity from one node on the server. In finance, a suddenly large transaction in an otherwise inactive account is an anomaly that most businesses would investigate.

While some outliers are due to data preprocessing errors, others might point to genuine fraud or cybersecurity incidents. Software engineers, especially AI developers, must identify and resolve these points, as they can compromise the predictive ability of a machine learning model.

Data scientists work with several types of anomalies when analyzing data:

  • Global outliers are any data points that lie far away from the others in a dataset.
  • Collective outliers are an entire subset of data points that diverge from the average.
  • Contextual outliers are diverging points that show up in places we don’t expect given the context. For instance, a seasonal business may receive a burst of extra revenue during certain parts of the year.

If software developers want to make accurate predictions based on the given data, they must identify anomalies and resolve them for a more reliable model.

Applying Anomaly Detection Models in Machine Learning

Anomalies detection occurs with the assistance of machine learning tools because:

  • The sheer volume of data is too large to go through manually
  • Most data is unstructured, so traditional software struggles to analyze it
  • Businesses must collect, clean, and analyze that data

Machine learning techniques typically generate the best results in this regard and are the most efficient anomaly detection method.

There are 3 primary approaches to developing ML-powered anomaly detection models:

  • Supervised– A data scientist goes through the training dataset and manually categorizes points into normal and abnormal categories. The model then extracts patterns from the data and attempts to characterize what an “abnormal” data point looks like. This approach relies on high-quality data and significant manual labor in collecting and labeling all that data.
  • Unsupervised– This approach does not use manual labeling. Unsupervised models detect anomalies by using what they learned in previous experiments on new input data. Neural networks are the most well-known example of unsupervised machine learning. While impactful, these networks essentially function as a black box, and making adjustments afterwards isn’t always easy.
  • Semi-supervised– A semi-supervised model combines both strategies. Engineers may personally supervise otherwise unsupervised learning to control the patterns the model picks up. The end result is a more accurate model.

Examples of Machine Learning Algorithms in Anomaly Detection

The specific techniques these algorithms employ include the following:

  • Local outlier factor (LOF) the most common technique, compares the local density of a data point with those of its neighboring points. Any point with a considerably lower density is likely an outlier.
  • Support vector machines (SVM) use hyperplanes in a multi-dimensional space to classify data points. This supervised model learns what “average” points look like and can identify anomalies accordingly.
  • DBSCAN or Density-Based Spatial Clustering of Applications with Noise, is an unsupervised algorithm that clusters data points together based on local density. Any points that do not end up in a cluster may be outliers.

Anomaly detection in data works with other methods too, like Bayesian networks and K-nearest neighbors.

Use Cases of Machine Learning-Powered Anomaly Detection

The examples of business use cases for anomaly detection are numerous.


Business networks contain a plethora of confidential information and intellectual property belonging to the company, its employees, and its clients. Networks are a popular target for cybercriminals, so companies must rely on intrusion detection systems to detect suspicious traffic going through their networks.

Anomaly detection is naturally an excellent fit. Specific compliance standards like SOC 2 even mandate anomaly detection as part of a compliance strategy. Such a model can promote adherence to data privacy regulations by ensuring data is secure as it moves through the network.

Detecting Financial Fraud

Organizations across the financial sector—including banks, loan providers, credit organizations, and insurance companies—use machine learning to detect potentially unlawful financial activities and fraud.

In the example of the loan provider, the business may perform a credit check before granting a loan. Machine learning models can point out anomalies in the submitted documents to detect whether they are fraudulent.

Catching Manufacturing Defects

Manufacturing facilities use machine learning to empower their quality assurance systems, especially in high-risk industries where defective products can result in costly lawsuits and a loss in trust in the company.

We’ve seen manufacturing facilities combine anomaly detection with on-site sensors and computer vision tools to detect whether a product contains unusual characteristics compared to the others on the production line. Engineers can pick out potentially defective products accordingly.