What is anomaly detection?

Anomalies aren’t necessarily good or bad, but companies should know about any break in pattern to assess whether or not they need to take action.

Businesses generate millions of data points during day-to-day operations, but a lot of that valuable information goes unused and forgotten. That’s why anomaly detection is growing in prominence in the business world: to optimize operations and streamline processes for a more predictable future.

What is the difference between anomalies and outliers?

Many business users use the terms anomaly and outlier interchangeably, but there are key differences. Anomalies are similar, but not identical, to outliers.

Assuming that all data is generated by a set of processes, outliers are points with a low probability of occurrence within a given dataset. They are observation points, distant from other observations within the normal population. However, outliers don’t necessarily represent abnormal behavior or behavior that occurred because of a different process. Outliers are generated by the same process but occur with a lower probability.

Conversely, anomalies are patterns that are generated by different processes. These different processes can alert a business that something has changed and may require further action, like equipment failure or fatigue.

Sometimes it takes judgment and subject matter expertise to determine which category a particular data point represents.

What is the value of anomaly detection?

Every day, businesses generate massive volumes of data. If leveraged correctly, that data can help businesses make better decisions, faster. One way is through anomaly detection. Early identification of anomalies can prevent minor issues from escalating into widespread, time-consuming problems. By leveraging machine learning techniques, organizations can monitor trends, pinpoint opportunities and risks, and gain a competitive edge through anomaly detection.

How does it work?

There are many technology capabilities and solutions that can be used to detect anomalies in real time, or even predict them, in some cases.

Visual discovery

Data or business analysts build data visualizations to find unexpected behavior, often requiring prior business knowledge and creative thinking, to find the answers with the right data visualizations. Advanced visualizations like those generated from Principal Components, TSNE, and UMAP can make high-dimensional data accessible through lower-dimensional maps.

Supervised learning

Supervised learning uses persons with business knowledge in a particular industry to label a set of data points as normal or anomalous. An analyst then uses this labeled data to build machine learning models to predict anomalies in unlabeled new data.

Unsupervised learning

Unlabeled data is used to build unsupervised machine learning models to predict new data. Since the model is tailored to fit normal data, the small number of data points that are anomalous stand out.

Time series techniques

Anomalies are detected through time series analytics with models that capture trends, seasonality, and levels in time series data. When new data diverges too much from the model, either an anomaly or a model failure is indicated.

Autoencoders and machine learning

The latest machine learning techniques and autoencoders detect and respond to anomalies in real time. A neural network can predict anomalies from transactions and sensor data feeds.

Clustering

Analysts can attempt to classify each data point into one of many pre-defined or discovered clusters; cases that fail to fall into known clusters can be considered anomalies.

How is anomaly detection currently used?

Across nearly every industry, there are many important business use cases for anomaly detection. A few of the most common examples are in Insurance, Financial Services, Healthcare, and Manufacturing:

• Financial crimes
• Equipment sensors
• Healthcare fraud
• Manufacturing defects

Fighting financial crime

In finance, trillions of dollars worth of transactions are executed every minute. Identifying suspicious banking transactions in real time can provide organizations with a competitive edge. To identify abnormal transactions, clients, suppliers, and leading financial companies have increasingly adopted big data analytics, including machine learning techniques, to detect anomalies among the voluminous sea of data being generated.

Additionally, leading financial companies can control costs with anomaly detection to save by eliminating false positive investigations and reducing fraud losses.

Monitoring equipment sensors

Many types of equipment, vehicles, and machines now have embedded sensors. For example, your smartphone has many, including ambient light and back-illuminated sensors, accelerometers, digital compasses, gyroscopes, proximity, NFC, GPS, and fingerprint sensors. Monitoring sensor outputs can be crucial to detecting and preventing breakdowns and disruptions.

Data-driven manufacturers can keep track of all their equipment, vehicles, and machines in real time with connected Internet of Things (IoT) devices. They can monitor all their outputs with an anomaly detection solution to prevent costly breakdowns and disruptions. Additionally, they can identify anomalous data patterns that may indicate impending problems by employing unsupervised learning algorithms like autoencoders.

Healthcare claims fraud

Insurance fraud is common in the healthcare industry, amounting to billions of dollars paid to fraudsters. It is vital for insurance companies to identify fraudulent claims to ensure that no payout is made to fraudulent accounts. In the past few years, many companies have invested heavily in big data analytics to build supervised, unsupervised, and semi-supervised models to detect insurance fraud.

With big data analytics and anomaly detection capabilities, healthcare and insurance providers can build supervised, unsupervised, and semi-supervised models to reduce the likelihood of healthcare fraud for each claim submitted.

Manufacturing defects

Some companies continuously monitor sensor data on manufactured components with an autoencoder model. As the model scores new data, technicians quickly detect and resolve any defects (anomalies) right as they happen.

Checking for defects and anomalies manually can waste time and increase costs for manufacturers, which is why many leading manufacturers are starting to use autoencoders. Companies can use sensor data on manufactured components to monitor and detect any unusual events in real time, using an autoencoder model.

More use cases

Beyond these most common use cases, many other industries also use anomaly detection:

• Military surveillance: Image recognition
• Cybersecurity: Intrusion detection
• Safety systems: Fault detection
• Hacking protection: Anomalous network traffic detection
• Weather: Heat wave or cold snap implications
• MRI imaging: Alzheimer’s or malignant tumor implication
• Spacecraft sensors: Faulty component identification

What does the future look like for anomaly detection?

Today’s data is increasingly growing and businesses are collecting more information than ever—with predictions calculating even more data growth in the future. With such a wealth of data, businesses must be able to track patterns and, more importantly, detect anomalies to avoid major business failures, like malfunctioning equipment, fraud, and defects.

Detecting anomalies in data patterns can help businesses uncover actionable insights and become more efficient and competitive in the digital age. With data science software, organizations can use machine learning models to specify expected behavior, monitor new data, and find unexpected behavior for better business outcomes.

Where might anomaly detection take us next? With the growing use of machine learning and artificial intelligence, detecting machine or sensor anomalies won’t be the only major use case. Experts predict that anomaly detection will continue to grow in prominence in video surveillance, healthcare diagnostics, and much more.