What is Anomaly Detection?

July 15, 2025

Anomaly detection identifies unusual patterns in data, helping spot errors, fraud, or system issues before they cause major disruptions.

Anomaly detection is a process that uses AI to identify unusual behavior or activities, and they hugely vary from the ordinary behavior of a dataset. These abnormalities might show some extreme conditions, including fraud, flaws, or errors.

They are known as outliers, and they signify potential issues, including security breaches, malfunctions of equipment, or fraudulent activities.

Anomaly detection is nothing new, as it has a history in statistics that is driven by scientists and analysts who observed charts to discover components that seemed to be different. According to the report of a renowned credit card processor, anomaly detection helped detect previously untraceable fraud patterns, and it resulted in the prevention of $500 million in fraud loss.

Table of Contents

Different Types of Anomalies

Let’s understand the different types of anomalies, those unusual patterns that signal something is off and help us catch problems before they escalate.

1. Point Anomalies

Point anomalies can either be unintentional or intentional and might also result from noise, mistakes, or exceptional occurrences. These anomalies are very common and can be identified because the point breaks the probable pattern clearly. An instance of point anomalies is bank account withdrawals that are remarkably larger compared to the previous withdrawals of a user.

2. Contextual Anomalies

Also called conditional anomalies, contextual anomalies are data points, and they diverge from the norm within a particular context. However, contextual anomalies might seem normal if they are viewed outside of that context or in isolation. For instance, in home energy usage, when there is a frequent escalation of energy consumption at midday when no family member is present in the home, then this anomaly is regarded as contextual.

3. Collective Anomalies

Collective anomalies are some data points that become anomalous when they are considered together. Commonly, there are some sequences of events or patterns of behavior that, when seen as a whole, seem unusual even when the individual element of that pattern isn’t anomalous inherently. To explain collective anomalies, an instance will be the network traffic data set when it displays an unforeseen surge in traffic from various IP addresses simultaneously.

Common Techniques Used in Anomaly Detection

Anomaly detection uses various techniques to spot unusual data patterns. These methods help identify errors, fraud, or system failures early on. Let’s get to know more in this section.

1. Supervised Anomaly Detection

Supervised anomaly detection techniques use labeled datasets, and here, instances are classed as either anomalous or normal for training a model that can recognize anomalies in new and unexplored data. This technique of anomaly detection is effective to detect some known kinds of anomalies, though it needs comprehensive labeled data. However, it can struggle with some novel anomalies also.

2. Semi-supervised Anomaly Detection

The techniques of semi-supervised anomaly detection extract the best features of both supervised anomaly detection and unsupervised anomaly detection. Semi-supervised anomaly detection provides an algorithm that has some parts of labeled data, and it can be trained partially too. Data engineers use these partially trained algorithms for labeling a bigger data set autonomously, which is acknowledged as “pseudo-labeling.” It is assumed that they provide dependable data points; hence, they are integrated with the actual data set for fine-tuning the algorithm.

3. Unsupervised Anomaly Detection

Data engineers use the unsupervised anomaly detection techniques for training models by supplying them with unlabeled data sets. It helps them to find out abnormalities or patterns on their own. Though these techniques of anomaly detection are regarded as the most commonly used because of their wider application, they need huge data sets as well as computing power. You will see the unsupervised anomaly detection technique to be prevalent in a deep learning scenario, as it hugely depends on an artificial neural network.

Use Cases of Anomaly Detection

Anomaly detection plays a pretty important role across different industries. In this section, we dive deeper into the many ways anomaly detection is applied in real-world scenarios.

1. Banking & Finance

The models of anomaly detection are used in the insurance, stock trading, and banking sectors to identify suspicious activities in real time, including money laundering, unsanctioned transactions, bogus tax return claims, credit card fraud, and unusual trading patterns. A good example is the escalation of credit card fraud. It is expected that by 2026, the value of losses that can pop up from credit card fraud is hoped to reach $39.6 billion. In this scenario, timely detection with the help of anomaly detection can help avoid these kinds of fraudulent activities.

2. Manufacturing

The algorithms used in anomaly detection are often combined with computer vision to identify defects in packaging by analyzing sensor data, production metrics, and high-resolution camera footage. By sensing deviations from standard operating situations, manufacturers prevent faulty products from making advancements. This approach improves quality control, rationalizes processes, and finally enhances the general operational excellence of the manufacturing sector.

3. Energy and Transportation

In the field of energy and transportation, anomaly detection identifies uncommon patterns or deviations that habitually indicate potential failures or issues. It is accomplished through different techniques, like machine learning, deep learning, and artificial general intelligence, as they analyze data from systems, networks, and sensors. When anomaly detection is used to keep a tab on energy consumption patterns, it results in more effective energy management.

4. Cybersecurity

IDSs (Intrusion Detection Systems), along with other cybersecurity processes, use anomaly detection to identify doubtful or unusual user activities. They indicate potential attacks, including malware infections or unsanctioned access. Anomaly detection monitors user behavior, network traffic, and system logs. This early detection helps in timely responses besides lessening data loss and potential damage.

5. Ecommerce and Retail

Merchants use an anomaly detection model to find out about distrustful behavior of customers. This helps them predict customer churn, detect fraud, and enhance marketing policies. In the sector of ecommerce, anomaly detection is used to identify account takeovers, fake reviews, unusual purchasing behavior, and other indications of cybercrime or fraud.

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