Why is it important

In the world of finance, detecting anomalies can often lead to the prevention of fraudulent transactions. Fraud transactions can cause huge losses, and hence seeing them as fast and as efficiently as possible becomes crucial. 

Anomaly Detection and It’s Importance in Business

Person looking at desktop trying to find anomaly detection

What is anomaly detection?

Have you ever wondered why Google keeps notifying you when you log-in through different sources, or when your spending pattern suddenly changes, why you get a notification from your credit card company?

Events such as these, which are either rare or do not follow the usual pattern, are considered anomalies. Detecting such circumstances is a significant part of fraud prevention, disaster prevention. 

As humans, we always look out for patterns – you are probably familiar with usual, ordinary, regular, which denote the status quo. But we are also looking out for sudden changes that are not usual. Unusual events that do not subscribe to the typical pattern often give us critical information! For example, if a website has a sudden influx of visitors, it could be a potential cyber-attack, or if the air pressure in the atmosphere drops suddenly, it could lead to a storm. 

In the world of academia, such anomalies can often lead to beautiful discoveries. But in the business world, we rarely want things out of order. 

The faster and more accurately we can discover such anomalies, the better we can prevent something unwanted.

Business problems and use cases

Anomaly detection can solve many business problems. 

In the world of finance, detecting anomalies can often lead to the prevention of fraudulent transactions. Fraud transactions can cause huge losses, and hence seeing them as fast and as efficiently as possible becomes crucial. 

With the increasing amount of IoT being used for different industrial needs, anomaly detection can prevent hardware breakdowns, network failures, and even accidents of a much larger scale. Large scale industrial plants often collect sensor data. Detecting anomalies in these signals is useful in ensuring that all the equipment is in optimal working conditions. We are also increasingly getting familiar with self-driven cars – and anomaly detection in such systems is critical for the safety of everyone in the vicinity. 

Cyber-attacks can often be prevented by continuously processing signals from network traffic and detecting any anomalies.   

We can see that anomaly detection has played a part in assuring safety and quality in almost every field. But as more and more businesses increase their online activities, both the volume and financial stakes have gone up. There is an increased effort to improve the safety and quality of service by creating better anomaly detection models.  

Anomaly detection with machine learning

With the importance of anomaly detection in almost every business domain, we are continually trying to improve the existing anomaly detection models. AI pushed us ahead by quite a few steps in this quest by providing machine-learning and deep-learning-based anomaly detection models. 

It’s easier for human beings to detect an irregular observation if we look at only one variable – say amount spent through credit cards. We can raise a red flag if the spending suddenly becomes much more than usual. But that alone is not always a good indicator of an anomaly. Other essential variables like spending, what is being bought, the time of the transaction also play an essential part. A data-point may look regular if we look at each variable separately – but we might discover it’s an outlier when we look at all the variables together. But it’s not an easy task to look at high dimensional space and detect such outliers. 

Machine-learning based models can do it. 

Types of Anomaly Detection

Machine learning-based anomaly detection can be of broadly three types:

  1. Supervised: The anomalies are labeled data points, so we know what a possible anomaly looks like. These are mostly unbalanced datasets – with a minor number of data points being anomalous. 
  2. Unsupervised: Where anomalies in the form of outliers are present but not labeled. The model needs to detect such outliers based on how different they are from most data points. 
  3. Clean: This one is slightly difficult even for machine-learning-based models. This group of problems has a data set with only clean, regular, and non-anomalous data points. So the model does not know what an anomaly looks like, as it hasn’t yet seen them. It can only observe regular data points and, based on that, detect an anomalous moment as and when it occurs later.   

Conclusion

Thus we can see how AI helps the world be a safer place by doing its part. In Aryng, we use machine learning and deep learning-based algorithms to make anomaly detection models and help our clients. You can read about them more in our case studies. 

You can read more about anomaly detection by visiting this website – https://en.wikipedia.org/wiki/Anomaly_detection#:~:text=In%20data%20analysis%2C%20anomaly%20detection,the%20majority%20of%20the%20data.

Our 33-pt proprietary BADIR framework can help solve business problems with machine learning and deep learning-based solutions. You can read more about BADIR here.  

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