Ever wondered how banks catch fraudulent transactions or how machines detect faults before they cause big problems? The answer lies in a powerful technique called anomaly detection. But what exactly is anomaly detection, and why is it so important? Let’s explore what anomaly detection is and the different techniques used to find these hidden anomalies.
Anomaly detection is a process of identifying events, identities, and data points that are outside the normal range. It is anything that deviates from the standard expected thing. It is also known as the outlier or novelty detection. This detection method is used to detect suspicious events, unexpected opportunities, and insufficient data buried in the time series. If there is suspicious data, then it will indicate fraud, crime, network breach, or faulty equipment.
Various types of detection methods involve intentional or unintentional errors. Some of the important types of detection are as follows.
Global outliers are also known as point outliers. They are anomalies in which a single data point deviates significantly from the rest of the data which can be either intentional or unintentional errors. They can be detected without considering a particular context or relationship within the data, such as a single unusually high transaction in financial data.
Contextual outliers are known as conditional anomalies. This is because these anomalies can be considered anomalous in a specific context but might be expected in other contexts. The contexts can be based on data points, location, time, or any other relevant factors.
A deviation from the norm is a set of data with no changes in the individual instances. A deviation from the expected pattern is a group or collection of data that deviates together from the expected pattern, and the individual points may not be anomalous.
Anomaly detection is a systematic process that involves various steps to ensure that anomalies in the data are accurately identified. Each step plays a significant role in anomaly detection.
This step is considered the first and foundational step in the anomaly detection process. It involves gathering all the relevant data from various sources, such as logs, transactions, sensors, user activities, and more.
It is a necessary process that involves a critical step of preparing the raw data for final analysis and modeling. It involves the substeps of cleaning, which are identifying and correcting errors in the data. Another substep includes feature selection and identifying the most relevant features in the dataset. Scaling of data is also a part of data preprocessing. It is mainly done when the data includes various units and scales.
In this step, appropriate anomaly detection algorithms for the given dataset are selected. Models such as z-score, Grubbs test, and others are selected based on their strengths and weaknesses and various factors such as the type of data, the nature of anomalies, and other requirements.
Model training is when preprocessed data is fed into a selected model to learn patterns of anomalous and normal behaviour. The model is trained in the dataset to identify anomalies and deviations from the learned patterns in the data with explicit labels.
Model evaluation is a process of assessing the performance of the trained model. It involves various metrics such as recall, precision, F1-score, and other areas under the receiver operating characteristics. This step helps identify any overfitting or underfitting issues.
Model deployment is considered the final step in which trained and evaluated models are integrated into the production environment. It involves setting up the necessary infrastructure to collect data, preprocess it, and run the anomaly detection model. In deployment, data will be continuously monitored over time and updated as needed to maintain accuracy.
The anomaly detection market, valued at USD 5.78 billion in 2024, is projected to grow at a compound annual growth rate (CAGR) of 16.22%, reaching USD 13.75 billion within the next five years.
The method is very useful as it seems to be promising for the businesses. But, it is equally important to know in which segment this detection technology will be helpful for business. So, let’s understand some of the importance of anomaly detection in businesses.
It is essential to detecting fraud and other such activities in transaction data. If fraudulent information is detected earlier, it can significantly reduce financial losses and protect the consumer’s information. It is mainly helpful for banks, insurance companies, and other such stakeholders. In various other sectors too, anomaly detection can help in determining the frauds and other such activities for the company.
Producing quality products is essential for industries such as pharmaceuticals and manufacturing. If there were anomaly detection systems in these industries, then they could identify the defects in aviation at the early stages. It would ensure that the products that meet the required standards are released in the market.
The ability to detect anomalies will also help in cloud cost management. It helps engineers and finance teams identify and analyze the root cause of significant changes so that they can easily take proactive actions in their cloud costs. It will help companies analyze and track cloud expenditures at a granular level and identify anomalies in real time.
If faulty versions are released in the market, it will affect the customer support process and prevent proper customer experiences. So, when anomalies are detected at the early stages, it will help companies react before the lapses occur and before they affect the user experience.
Anomaly detection plays a significant role in safeguarding and optimizing various aspects of business operations. There are various types of anomaly detection that require various approaches to highlight the complexities and diversity. It helps in understanding critical elements such as data analysis and monitoring that allow the identification of any unusual problem and also provides room for improvement in the companies.
Q: What is anomaly detection?
A: It is a method or a process of detecting and identifying data points, events or observations that deviate significantly from normal behavior. It helps to detect the frauds or any other issues in the data set.
Q: What are the three main types of anomaly detection?
A: The main types are global anomaly, contextual and collective anomaly. These types help in analyzing the data either collectively or individually to analyze the fault in the data set.
Q: How to detect an anomaly?
A: When there is a data set, there is a pattern in the data. For instance, there would be credit card transactions consistently for a certain amount at a certain location. However, when there is a denser transaction with high amounts which is different from other data sets, then there is an anomaly.
Q: What role does machine learning play in anomaly detection?
A: Machine learning helps in both supervised and unsupervised manner for using the patterns in data and identifying the anomalies based on these patterns.
Q: Can anomaly detection be automated?
A: Yes, it can be automated using machine learning algorithms and other such software tools.