Almost every business generates enormous amounts of data, and those who still don’t know how to utilize this data to survive and thrive in the data-driven world may go out of business soon. By utilizing data, we mean using it for anomaly detection, one of the significant use cases of artificial intelligence, machine learning, and data analytics- technologies that are leveling up the data game for global startups and enterprises. 

While there are numerous other use cases of these technologies, this blog is solely focused on the most underrated one: anomaly detection. Apart from explaining AI anomaly detection in detail, we have covered its benefits, methods, use cases, and technologies that run in the background to make anomaly detection possible. 

Without further ado, let’s begin. 

What is Anomaly Detection

In simple terms, anomaly means something different, and anomaly detection means detecting what’s different. In short, whatever doesn’t fit the normal data gets flagged as an anomaly. 

In technical terms, AI anomaly detection is the process of analyzing events and items and making observations to find their difference from the majority of the data. This analysis or anomaly detection is done to find errors, fraud, network intrusions, and equipment failures or issues. By flagging these unusual occurrences, anomaly detection makes further investigation and action-taking easier and precise. 

AI Anomaly detection examples are: 

• A fraudulent transaction in banking
• A machine fault in manufacturing
• A cyberattack on a network
• A health problem in patient monitoring

Anomaly Detection: Market Statistics 

Before moving on to the use cases of anomaly detection, let’s check out some interesting market statistics: 

• The global anomaly detection market size is anticipated to reach approximately USD 28.00 billion by 2034. 

• The anomaly detection market is projected to generate a revenue of $6.90 billion in 2025.

• North America holds the largest share with over 32% revenue share last year.

• The market is driven by the increasing risks of cyber threats, mainly in healthcare, finance, and telecommunications.

Types of Anomalies You Can Detect with Anomaly Detection 

To understand anomaly detection thoroughly, it is crucial to go through different types of anomalies that can be detected using this technology. Let’s take a look at the same:

#1 Point Anomalies 

Also known as global outliers, this type of anomaly means detecting a single point that is way too different from the expected range. For example, a person who usually withdraws an average of $3000 per month suddenly withdraws $10,000 may be considered an anomaly. 

#2 Contextual Anomalies 

This type of anomaly detects unusual data points in a particular time, location, and condition. It is also known as a conditional anomaly or outlier. For instance, a smart meter detects high consumption of electricity past midnight in a household. 

#3 Collective Anomalies 

Also known as pattern outliers, collective anomalies detect a group of data points that are normal when done individually, but abnormal when observed together. For example, login attempts of a single account from multiple devices using different IP addresses at the same time can indicate a cyberattack and, hence, be detected as an anomaly. 

You may like to read: How Does AI Video Analytics Help Detect Threats in Real Time?

#4 Spatial Anomalies

Spatial anomalies detect unexpected or unusual patterns in location-based data. For example, anomaly detection when implemented into surveillance systems can detect people entering restricted areas in a facility. 

#5 Temporal Anomalies 

Time-series or temporal anomalies are responsible for detecting unusual changes in time-based data streams. For instance, an anomaly is detected if the system finds a sudden drop in website traffic during peak business hours. 

How Anomaly Detection Works

Anomaly detection works by learning the normal patterns or behaviors within a specific set of data. It uses various methods that can be statistical, ML-powered, or hybrid. Anomaly detection, when done using ML, requires training models on historical data to comprehend normal behavior and flag deviations in the new data. 

Anomaly detection systems or solutions can detect unexpected events that don’t match normal behavior. They trigger an immediate alert to facilitate real-time response and decision-making. 

Difference Between AI Anomaly Detection and Traditional Anomaly Detection 

Aspect	Traditional Anomaly Detection	AI-Powered Anomaly Detection
Approach	It follows a rule-based approach with a statistical threshold.	Leverages machine learning and deep learning models that learn patterns automatically.
Data Requirements	It can handle small and structured datasets.	AI anomaly detection can efficiently work on large, complex, and even unstructured datasets of different formats and types.
Adaptability	This type of anomaly detection requires manual updating in case of a change in patterns.	AI-powered anomaly detection can automatically and continuously learn and adapt to evolving data and behaviors.
Precision	Limited	Highly accurate
Use Cases	Can be used for quality control, simple fraud checks, and threshold alerts.	Some use cases of artificial intelligence anomaly detection include cybersecurity, predictive maintenance, real-time surveillance, and healthcare monitoring.
Scalability	Scalability is a challenge in traditional anomaly detection.	This can be easily scaled to big data and real-time monitoring.

AI Anomaly Detection Use Cases: 7 Unique Applications

#1 Fraud Detection 

Anomaly detection can be used to detect unusual patterns in transactions that may result in fraud or financial loss. For instance, a credit card company can use an anomaly detection system to review transactions made by a credit card holder using their card in some other country. 

Read More: Fraud Detection System Development: A Comprehensive Guide

#2 Network Security and Intrusion Detection

Cybersecurity service providers can utilize anomaly detection to monitor traffic and find suspicious activities that look different from normal network traffic and behavior. For example, a company’s firewall with anomaly detection capabilities can detect a sudden increase in the transfer of data from an internal server to an unrecognized or unauthorized IP address. This could result in a data breach. 

#3 Quality Control 

Anomaly detection systems, when connected with quality control systems and IoT sensors, can automatically detect defects or abnormalities in equipment before they turn into large-scale failures. For instance, anomaly detection can work together with a production line to observe even slight vibrations in equipment or the conveyor belt motor. Based on the alert, enterprises can take preventive measures. 

#4 Healthcare Monitoring 

With real-time anomaly detection in CCTV surveillance for healthcare monitoring, hospitals and healthcare institutions can monitor patient vitals and mark anomalies that may lead to injuries or health risks. For instance, a heart monitor can send an immediate alert to the medical professional in case the patient’s heartbeat is irregular. 

You might be interested in: AI in Remote Patient Monitoring: Improving Healthcare Accessibility and Convenience

#5 Demand Forecasting 

Retail companies can leverage anomaly detection to identify unusual sales patterns or signs of theft or sudden demand. Based on the detected anomalies, retailers can make necessary adjustments. For instance, if an anomaly detection system, working with the returns management system, detects an increase in the number of returns of a particular product, it will prompt an immediate investigation into the defect in that product. 

Explore More: AI in Demand Forecasting: Use Cases, Benefits, and Implementation

#6 Cybersecurity 

Anomaly detection can be used in cybersecurity to flag indicators of a possible cyberattack. For instance, an IT team working in a multicloud environment can easily detect anomalies to identify overlapping systems and configurations. Apart from cybersecurity, anomaly detection can also help IT teams to monitor servers, applications, and network infrastructure for irregularities such as traffic spikes, resource usage-related anomalies, and system failures.

You may like to read: Why AI Facial Recognition System is the Future of Cybersecurity?

#7 Surveillance and Security

Using anomaly detection for surveillance and security is one of the best applications of AI anomaly detection. It enhances security, situational awareness, and public safety by analyzing video feeds and images to spot unusual activities and objects and facilitate immediate incident response. 

You may like to explore: AI-Powered Video Analytics for Public Sector

Benefits of AI-Powered Anomaly Detection 

Some of the core advantages of anomaly detection powered by artificial intelligence are:

#1  Detecting and Preventing Issues at an Early Stage

With AI anomaly detection, businesses can identify unusual or unexpected patterns as well as behavior at an early stage. This prevents further escalation of those issues into big problems. Anomaly detection, mainly the one powered by artificial intelligence, also prevents operational disruptions while minimizing potential damage.

#2 Improved Operational Efficiency

With anomaly detection, businesses can optimize maintenance schedules and prevent costly emergency repairs or equipment/vehicle breakdown. In the case of software, anomaly detection can help prevent fraud-related losses. This also reduces the costs associated with the loss. 

#3 Better Security 

Improved security is one of the biggest anomaly detection benefits. It strengthens the security system by detecting unusual behavior, transactions, unauthorized access attempts, and malicious network traffic. Improved security prevents fraud, which can not only impact a business’s reputation but also result in loss of sensitive customer information and money. 

#4 Data-Backed Decision-Making

AI-powered anomaly detection flags anomalies and sends real-time insights and alerts to authorized personnel. With these alerts, authorities can take immediate action and build data-driven strategies for the future. 

#5 Seamless Scalability 

Anomaly detection systems with AI capabilities and ML algorithms can learn and detect anomalies in new data without manual intervention. It means they can scale without manually training them on new datasets.

Which Industries Can Leverage Artificial Intelligence-Backed Anomaly Detection

Anomaly detection use cases that have been mentioned in the previous section of this blog can be used in various industries, including banking, healthcare, manufacturing, retail, and e-commerce. Let’s explore more to know industry-specific use cases of anomaly detection:

#1 Banking and Finance

It can be used to detect unusual financial behavior to prevent fraud and ensure adherence to industry regulations and compliance. 

• Fraudulent transaction detection
• Money laundering pattern identification
• Credit card charge-related anomaly alerts

#2 Healthcare

The healthcare industry can leverage artificial intelligence anomaly detection to monitor patient health data and medical operations to find abnormalities or risk factors. 

• Irregular heartbeat or vitals
• Abnormal lab test results flagging
• Medicine overdose detection

Similar Read: AI Fall Detection for Hospitals and Elderly Care

#3 Manufacturing

AI anomaly detection in manufacturing contributes to ensuring uninterrupted production by detecting machine or equipment faults and quality issues. 

• Predictive maintenance for equipment
• Defected product or assembly line detection 
• Flagging abnormal vibration in machinery

#4 Retail and E-Commerce

Anomaly detection in retail and e-commerce spots unusual sales patterns and website traffic to prevent revenue loss and cyberattacks.

• Sudden drop or spike in sales
• Cart abandonment anomalies
• Unusual website traffic 
• Bot attack alerts

Similar Read: Developing Queue Detection AI Systems for High Traffic Scores

#5 Telecommunications 

The telecommunications companies can leverage anomaly detection to monitor network traffic and usage patterns. 

• Detection of network intrusions and cyberattacks
• Abnormal data usage
• Fraudulent SIM card activity

#6 Energy & Utilities

AI anomaly detection for the energy and utilities sector can do wonders by optimizing energy usage and preventing outages with real-time monitoring. 

• Flagging a sudden spike or drop in utilities
• Detecting faults in power grid equipment
• Predicting abnormal demand usage

Read More: AI in the Energy Sector: How AI Enhances Resource Management and Sustainability

#7 Transportation and Logistics 

Implementing AI-powered anomaly detection in the transportation and logistics industry can ensure the safety of the fleet and shipments. 

• Detection of unusual route deviation of delivery vehicles
• Detection of anomalies in cargo temperature 
• Abnormal fuel consumption alerts

#8 Surveillance and Security 

With AI anomaly detection in surveillance and security, businesses and security service providers can enhance safety by identifying suspicious activities in real-time. 

• Suspicious activities detection
• Unauthorized entry detection 
• Loitering in unusual locations

Read More: Suspicious Behavior Detection Systems in Banks: Complete Development Guide

Technologies and Techniques Used in Anomaly Detection 

Several technologies and techniques run in the background to make anomaly detection work. Here is a list of them:

#1 Statistical Methods 

This type of anomaly detection method relies on mathematical models and thresholds to flag irregularities. Statistical methods include z-score, interquartile range (IQR), Grubbs’ test, and Tukey’s range tests to deliver output. This type of anomaly detection works perfectly with small datasets. 

#2 Machine Learning Techniques

Another method that anomaly detection uses is machine learning. The technology enables an anomaly detection system to learn complex patterns from large datasets. From supervised learning and semi-supervised learning to unsupervised learning, anomaly detection uses these methods to identify anomalies across different data types. 

#3 Data Learning Methods 

Anomaly detection uses deep learning techniques and neural networks to process large, high-dimensional, and sequential data to detect anomalies. It uses autoencoders, long short-term memory networks, generative adversarial networks, and conventional neural networks to offer highly accurate detection of intricate patterns.

#4 AI and Big Data

Advanced anomaly detection utilizes artificial intelligence and big data technologies to get data from multiple sources and process it to detect anomalies. Besides, it also utilizes edge computing to process the data closer to the source. 

How to Implement AI-Enabled Anomaly Detection 

Now that you know almost every crucial aspect of anomaly detection, let’s take a look at the steps to implement it into your business processes or operations:

Step 1: Define Objectives and Use Cases

The first step is to define the objective for which you want to implement AI-powered anomaly detection. The objective could be fraud detection, predictive maintenance, cybersecurity monitoring, and others. 

Step 2: Data Collection and Preparation 

In the next step, collect relevant data from the systems or processes where you need to implement anomaly detection for any particular use case. These data sources could be sensor outputs, network traffic, medical records, and others. Prepare the data by cleaning, preprocessing, and labelling it to ensure quality and consistency. 

Step 3: Select the Right Anomaly Detection Technique 

Choose the right anomaly detection technique considering the type and volume of data. You can choose from statistical, machine learning, and deep learning methods that have been described earlier in this blog. 

Step 4: Model Development and Training 

Create custom AI models using historical data. Depending on the technique you chose for anomaly detection, train models on labeled or normal data. Utilize cross-validation and performance metrics to evaluate the performance and accuracy of the model. 

Step 5: Integration and Deployment

Now, integrate the developed anomaly detection system into your current workflows or operations. Properly set up continuous data streaming pipelines to facilitate the feeding of fresh data into the model automatically. 

Step 6: Monitoring and Maintenance 

The last step of anomaly detection system development is to regularly monitor the model’s performance against defined KPIs. Continuously train the model on new data to ensure accurate detection of anomalies. 

If you don’t want to try your hand at anomaly detection system development, contact a reputed AI development company with prior experience in the same. 

Explore the Complete Potential of AI-Powered Anomaly Detection with Quytech 

Quytech is a leading AI development company with a background in building basic rule-based to advanced AI- and ML-powered anomaly detection solutions. These solutions are capable of processing vast datasets in different formats, images, videos, and texts, to detect anomalies and enable organizations to adopt a proactive approach. 

The anomaly detection systems that we have developed have helped organizations to reduce security risks, identify fraud, prevent equipment or machinery failures, and optimize operations. We have built over 100 AI-powered anomaly detection systems for retail, e-commerce, healthcare, banking, finance, manufacturing, and other industries. Some of our top anomaly detection systems include an AI-powered fire detection system, a fall detection system, and a suspicious behavior detection system.

Final Words

Anomaly detection using AI goes way beyond ordinary irregularity detection; it offers predictions to prevent the occurrence of anomalies. Traditional anomaly detection is rule-based and works on small datasets only, whereas AI-powered anomaly detection brings unmatched accuracy, speed, and scalability to make the system capable of automatically processing new data to detect complex anomalies. 

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