Recently, we have started to get an overview how you: a person working in tech / business owner or just a person passionate about Data Science (there must be people interested for no obvious reason) can use Machine Learning tools in day-to-day business operations. 

Previously, we’ve discussed how Machine Learning can be used to predict customer churn and how it can help us build recommendation engines across different industries. But why stop there? 

In the present article we will discuss how Machine Learning can be applied in anomaly detection. We will be focusing mainly on how this can be applied to detect fraudulent activity.

Anomaly detection i.e “Stop that Fraud” 

What is it?

Because of Machine Learning’s capacity to understand and determine patterns, it is able to quickly identify anomalies in data that fall outside of predicted patterns. Not surprisingly, this makes a valuable tool for detection of fraudulent activity.  

This has especially been a useful tool for working with financial transactions. All businesses depend on payments from their customers and it is crucial that payments come from legitimate buyers, otherwise businesses open themselves up to expensive processes such as chargebacks and poor customer experience.  

Traditionally businesses relied on good old “rules” to block fraudulent activity. This could take a form of blocking transactions for high-value orders or taken from within high-risk locations.  

However, there are many complications with this rigid “all-or-nothing” approach. Having fixed outcomes means that genuine customers placing large orders and buying in high-risk locations would be blocked, which creates an obvious problem for both the customer and the business. Additionally, as fraud evolves and becomes more sophisticated, maintaining the increasing number of manual rules, which can get very difficult to maintain very quickly. 

While there is certainly a place and time for “rule-based” approach and it cannot be abolished completely, Machine Learning provides an invaluable upgrade to the fraud detection system.The techniques can be extremely effective in fraud prevention and detection, as they allow for the automated discovery of patterns across large volumes of streaming transactions. If done properly, Machine Learning can clearly distinguish legitimate and fraudulent behaviours while adapting over time to new, previously unseen fraud tactics.

How does it work?  

As with any other Machine Learning pipeline, it all starts with Data.. 

Learning model has to collect data. 

After data is gathered, it is segmented into features that are describing “average” customer behavior. Examples of these features can be customers identity, order history, location and chosen payment method. This is what will help the model create the baseline of what “normal” purchasing behavior is for a particular customer or a group of customers. 

As with any Machine Learning model: the more features are added, the more complex the model will be, but also more sensitive it will be to anomalies. 

When the model has been formed, it needs to be “trained” on a training set of actual financial transactions, which will get it accustomed to what the real data looks like. The specifics of training will vary depending on the underlying “learning” model chosen: 

Supervised model- model has to be trained with transactions that are already classified as “fraudulent” and “non-fraudulent”. The model is trained by ingesting significant amounts of classified labels, which it uses as a base against which it will correlate future data. 

Unsupervised model- model uses data to self-learn the patterns in the data and separates it into different classes of transactions. 

Finally, after training is over, the model’s validity, effectiveness and accuracy can be evaluated through the testing process. If the model can reliably and accurately determine fraudulent transactions on the “test set”, it can then be applied in real-life setting. It is always up for a debate at what point you conclude that a model is “accurate enough” (don’t get me started on that). 

Where else Can I see it used? 

While, as discussed, anomaly detection is incredibly useful at detecting fraudulent payment transaction, there are other business areas, where fraud detection can be invaluable.  

• Insurance claims: using semantic analysis in fraud detection to analyse files written insurance claims to try and catch suspicious correlations in textual data. Similarly, duplicated claims and overstated repair costs can be picked up. 
• Medical care: Machine Learning algorithms can help pick up use of invalid fake diagnostics during claiming process and invalid paperwork.  
• Identity fraud: Image recognition can also be used as a fraud prevention strategy to detect misuse of personal documentation internationally and domestically. 

Where can I start? 

Overall, Machine Learning in anomaly detection can be very useful when it comes to identifying customer unusual activity, which can be applied in many different settings. 

You might be interested in applying anomaly detection to your business or there is always a chance that you are interested in building a fraud detection pipeline just for fun.. As always, where to start will depend on the resources and the technical knowledge you or your company has.  

If you have a team of in-house developers, then a great place to start can be Microsoft Cognitive Services, which will allow you to implement supervised and unsupervised machine learning models. 

However, if your business doesn’t have an internal technical support, then partnering with a company that focuses on predictive analytics, such as Coeo, can be great for your business.  

And we are always here for you, get in touch by emailing info@coeo.com.