Anomaly Detection in Machine Learning: How It Can Help Your Business

Anomaly detection in machine learning. These words in combination might sound too scientific and may intimidate some readers. But in fact, they refer to something that can empower your business, whether you’re a manufacturer, healthcare provider, IT professional, or a tech solution provider who wants to improve your products, services, or processes. But what do those words mean and how does machine learning for anomaly detection work? The answers are in this article.

First, let’s break down what anomaly detection means.

What is anomaly detection in machine learning? 

Basically, an anomaly is an outlier — something that deviates from the norm. For example, it can be a sudden burst of activity on a website, a grant applicant requesting much more or much less money for a project than expected, a production defect, or a breast scan tumor.

Anomalies fall into several types: 

• A global outlier is a data point with a very high or very low value compared to other data points in a dataset. Say that in Germany, the average temperature in July fluctuates between 59.4°F and 76.6°F, but one day it’s suddenly 41.2°F. The latter value is an outlier since it falls outside the normal temperature range.
• A contextual outlier is a data point that deviates from the rest of the data points within a certain context. For example, in big cities, traffic jams typically occur at the beginning or end of the work day (the context). However, a traffic jam at 9 p.m. is an outlier.
• A collective outlier is when a number of data points behave unexpectedly. For example, businesses don’t typically shut down simultaneously worldwide. But during the COVID-19 pandemic, many companies around the world did. That’s a collective outlier because such massive shutdowns don’t typically occur; this shutdown only happened because of the pandemic.

In general, an anomaly isn’t a sign of something good. For example, a sudden burst in activity on a website can indicate a DDoS attack, a tumor can be a sign of breast cancer, or a traffic jam at 9 p.m. might result from a volcano eruption in the area. Thus, identifying outliers and minimizing their negative impact is important. This process is known as anomaly (or outlier) detection.

Why anomaly detection is hard without machine learning (ML) 

Anomalies seem like they might be easy to detect because they deviate from what’s normally expected. But unfortunately, it isn’t always easy. In fact, outliers are often challenging to detect for several reasons.

Today we deal with huge amounts of data

To identify online banking fraud, an investigator would have to examine the characteristics of every single transaction, for example, a device used, IP address, a user’s physical location, username, and so on. Given the number of transactions generated every second, it would be almost impossible to derive any meaningful insight by manually searching through transactions.

Rule-based anomaly detection solutions can make it easier to identify fraud in a large number of transactions by applying rules like “if the location doesn’t match the IP address (the rule), the transaction is fraudulent.” But they don’t resolve the two following challenges.

Some anomalies evolve all the time

Even the most skilled virologist can’t name all possible coronavirus mutations because they evolve over time. Or let’s take another domain, for example — cybersecurity. Fraudulent actors constantly come up with new techniques. To catch them, you’d need to update your detection techniques nonstop.

Some anomalies can go unnoticed

Did you know that one-third of epileptic patients have typical-looking MRI brain scans? In other words, with conventional anomaly detection techniques alone, it’s impossible to perform the surgery to save a patient from seizures in one-third of epilepsy cases. 

Fortunately, ML has the potential to address these challenges, taking anomaly detection to the next level. 

How machine learning improves anomaly detection

Machine learning is an artificial intelligence (AI) approach that relies on computer algorithms to detect historical or sample data patterns. For example, Netflix recommendations are based on complex algorithms that analyze your previous program selections; surveillance data analysis tools can identify a burglar based on hundreds of videos of burglars.

ML solutions can significantly improve anomaly detection. Here’s how they address the anomaly detection challenges we described above: 

• Large amounts of data: ML can process huge volumes of data quickly. It can derive insights in real-time when new data appears.
• Evolving outliers: Some ML models learn to detect outliers more accurately with every new case. If you have such a solution, say, for banking fraud detection, you don’t have to build a new model every time a new fraudulent scheme emerges.
• Hidden outliers: Some ML models, called unsupervised models, can learn from data that aren’t explicitly labeled as “normal” and “anomalous.” This allows them to see anomalies where a human being can’t. 

Now let’s see ML’s anomaly detection capabilities in action. 

Some use cases of ML-based anomaly detection software

With its ability to process different data types, ML-powered solutions can detect anomalies anywhere — be it in an image, a video, or a database with numerical values — even if it’s harder than finding a needle in a haystack. Below we highlight a few use cases of ML for anomaly detection.

Medical image analysis

Healthcare heavily relies on medical imaging. Without CT, X-ray, ultrasound, and MRI scans, it would be impossible to diagnose, monitor, and treat many conditions. At the same time, the industry suffers from a lack of experienced radiologists to analyze and detect anomalies in the quantity of radiology scans generated today.

ML-powered solutions for anomaly detection in medical scans have the potential to fill the gap. What’s more, some solutions can help catch anomalies that even the most experienced radiologists (if unaided with tech) are unable to catch. For example, in a small retroactive study conducted by researchers from the University of California, an ML model detected signs of Alzheimer’s disease, with greater accuracy than human clinicians, in brain scans six years before the patients were finally diagnosed with Alzheimer’s disease. Although more research is needed to determine if the technique will prove to be clinically relevant, the technology is clearly promising.

Banking fraud detection

Fraudsters are constantly making up new ways to swindle transaction systems and are doing so quite successfully. According to the Federal Trade Commission, consumers lost $5.8 billion to bank fraud in 2021, which is 70% more than in 2020. A manual review of the vast number of banking transactions is not feasible. Rule-based fraud detection solutions are prone to reverse-engineering by thieves. So how is a banking service provider to protect their clients?

That’s where ML-powered anomaly detection software comes into play. These systems can instantaneously spot anomalies in large amounts of transactional data. For example, users of Capgemini’s ML fraud detection system have reported increases in their detection rates from 50% to 90%. Feedzai claims that its ML-powered banking fraud prevention software can boost your operational efficiency by 95%.

Programmatic ad fraud detection

Banking is not the only domain susceptible to fraud. For example, companies (advertisers) that publish their pay-per-click ads on third-party resources often fall victim to dishonest publishers who generate fake clicks. According to some estimations, robot clicks can amount to 90% of all registered interactions in an ad campaign. Just like financial fraud, manual approaches and rule-based solutions aren’t ideal for detecting fake clicks.

Meanwhile, ML anomaly detection solutions can easily spot anomalies induced by fake clicks. For example, one study demonstrated that logistical regression and Gaussian naive Bayes ML classifier algorithms can distinguish false clicks from organic ones with over 99% accuracy. Integrate these into an automated fraud prevention tool, and you’ll be able to weed out crooked publishers when they target you.

Product defect detection

Financial losses associated with a defective product can significantly impact a business, reaching inordinate sums. Case in point: it cost Samsung more than $5 billion to recall their Galaxy Note 7 line — the smartphones randomly caught fire and sometimes exploded when their batteries overheated. And that’s not even the most expensive defective product recall in history. Thus, it’s paramount that manufacturers ensure that their products meet all quality standards. 

But checking everything manually becomes problematic if you have large production lines; you need automation and machine learning. For instance, Amazon Lookout for Vision uses computer vision and ML to detect product defects. How does it work? Say you produce bearings for bicycles. A conveyor belt moves the bearings along, watched by a camera that takes a photo of each bearing. A computer vision algorithm analyzes the photo while an ML algorithm detects anomalies in that photo. Any bearing the ML algorithm deems defective is automatically pushed off the belt, and defective bearings never make it into the bicycle production line.

Defective equipment detection 

Manufacturers need to keep their equipment up and running. A broken product carrier can halt production while also posing a threat to personnel’s safety. At the same time, keeping product carriers in working order can be time-consuming for a manufacturer operating at scale.

For example, at Tyson Foods, an operator must inspect 8,000 pins per line to prevent product carrier failures. The manual inspection takes an eternity, so Tyson’s management streamlined it using Amazon Lookout for Vision. The result exceeded all their expectations: the model detected failing pins with 99.1% accuracy and saved one hour of team member time per day per line.

IT infrastructure maintenance

Maintenance of large IT infrastructures often involves one technician taking care of hundreds — if not thousands — of networked devices, and those devices produce an endless stream of measurements. The technician needs to monitor all this data and intervene whenever a failure occurs. How can they provide timely maintenance? By using machine learning.

However, IT infrastructure, with its complex device interactions and dynamic environments, doesn’t lend itself to using ML algorithms that rely on labeled data. Enter Google’s ML-powered anomaly detection software, which uses unsupervised learning techniques to train ML models to accurately differentiate outliers from normal working conditions. It monitors IT infrastructure, notifies a technician when issues arise, and even explains the issues to the technician.

Now you know the key advantages and capabilities of ML-powered anomaly detection. But are there any drawbacks? Let’s take a look.

Challenges in implementing ML-based anomaly detection

The potential of ML for anomaly detection is immense, no matter your business niche. But you’ll only see all the potential advantages if you implement ML correctly. And unfortunately, that’s not always easy because of some common challenges.

• Defining normal. To build a supervised ML model, you need to determine all the properties of normal conditions and of outliers. Sometimes this is challenging. 
• Accounting for your niche. Outlier detection is usually industry-specific. For instance, a very small deviation in clinical data can indicate an anomaly. Meanwhile, in marketing, outliers are usually indicated by more significant deviations.
• Understanding insights your model generates. Sometimes it’s challenging to understand why something has become an outlier or to determine if it’s a harmful anomaly or a benign one. That’s why it’s critical that your solution can justify why it marks this or that value as an outlier.
• Finding enough training data. The more data an ML model consumes, the more accurate the insights it generates. But too often, it’s difficult to gather enough relevant data. This is a problem, particularly for cybersecurity.
• Finding a suitable ML algorithm (or a combination thereof). ML algorithms aren’t all created equal, and each is specialized for specific cases. You need to find the one that works for your particular case.
• Identifying ML experts. Alteryx, Amazon, Microsoft, and other top players partially address this challenge by offering platforms that let users build ML solutions with little to no coding. However, like any prebuilt solution, they have limitations. You can’t avoid custom coding if you want to build something unique.

Luckily, don’t have to address these challenges alone. A reliable software developer with anomaly detection and machine learning expertise can help you with any issues that arise on your way to finding a perfect solution. Unicsoft, for example, can analyze your business case and design ML-based anomaly detection software that precisely fits your needs.

Conclusion

Any outlier — harmful or benign — is a treasure trove of valuable data that you can use to prevent a catastrophe or simply improve your business processes. Either way, detecting anomalies as early as possible is essential if you want to derive maximum value from them.

A powerful ML-based anomaly detection solution will empower your business. It can identify anomalies in medical imaging scans years before they become visible to a human eye. It can spot defective products before they negatively impact your manufacturing business. It can catch click ad fraudsters red-handed before they devour your marketing budget. Just imagine what it can do for you.

Whether you need a solution to improve your internal processes or are looking for ways to enhance your tech product, drop us a line to discuss your needs.