Shopify Ecosystem

How Machine Learning Is Used In Fraud Prevention For Ecommerce

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The progress of commerce business has closely followed the development of technology. From catalog or phone shopping to online shopping, from paper payments through credit cards to contactless payments, eCommerce has been developing steadily through the years. But, as we all already know, fraud won’t be far behind where there is money in play.

Fraudsters are constantly on the lookout for advances in technology and new eCommerce trends they can use for their own benefit. There is no limit to their actions, from exploiting merchants to gathering sensitive information they can use for their malicious schemes. While before, fraudsters had to get their hands on your wallet to steal your money, now they just need to get access to your online accounts. Just one password that is too obvious can be all that stands between a fraudster and your money.

Luckily, fraud prevention agents can also use technological advancements to stay ahead of the fraudsters. The development of technology has made old traditional fraud prevention methods obsolete as they can no longer keep up with the fraudsters. It is time for a change. For example, the combination of SEON’s fraud detection with machine learning allows them to recognize the patterns of fraudulent behavior and prevent fraud before it can cause any damage. 

Fraud in eCommerce

The truth is that one thing eCommerce merchants are familiar with is fraud and fraud solutions. When you work with money, you realize early on that there will always be people willing to take advantage of you and steal from you. Merchants never had the luxury of hoping they would not be affected by some type of fraud like some other businesses since they have always been a favorite target for fraudsters. Just in 2021, there have been 20 billion U.S. dollars in eCommerce losses to online fraud, which is 14 percent higher than the previous year. 

While many types of fraud can target the eCommerce business, such as malware or a data breach, the most common ones are chargeback fraud and account takeover.

Chargeback fraud: happens when a customer requests a chargeback from their bank or card provider after buying a product claiming that the transaction was fraudulent. This type of fraud can be the result of criminal activity such as fraudsters using stolen payment information or friendly fraud, where a customer has indeed made an order, but they have decided to request a chargeback. While it might happen due to valid reasons like misunderstanding with the merchant or not recognizing the transaction, it still causes significant damage, especially since it accounts for between 40% to 80% of all fraud losses according to Forbes. Chargeback fraud leads to the loss of revenue and inventory, paying additional fees, or even getting blocked by the bank since they are considered high risk. 

Account takeover: This happens when the fraudsters manage to breach the users’ accounts and then use them to purchase goods and services or even try to get money. By the time legitimate customers discover unauthorized transactions on their bank statements, fraudsters will already be long gone. 

Financial loss is not the only damage the business can experience as an aftermath of the fraudulent attack. They also pose a significant risk to their reputation, which can result in losing the customer as nobody wants to do business with a company they can’t trust. According to the research, 94% of consumers will avoid a company due to negative reviews. No eCommerce business is immune to these types of attacks, but they can be significantly reduced by using proper fraud prevention solutions.

Why is traditional fraud prevention becoming obsolete?

All eCommerce businesses are aware of the importance of fraud prevention, but a large number of them still don’t know what actions they need to take. The world of cybersecurity and fraud prevention can seem daunting, so they rely on traditional fraud solutions they have been using for years. While they are definitely better than having no protection at all, they are not enough to help merchants deal with the evolving dangers of online fraud. 

Most of the traditional fraud detection programs are developed as one-size-fits-all solutions, which might’ve provided an adequate level of protection in the past, but no longer. No eCommerce business is the same; how can their fraud protection strategy be the same?

The second problem with traditional fraud prevention is that they are linear and static, while fraud is anything but. Fraudsters keep evolving their methods, and fraud solutions need to be able to do just that in order to keep up and stay ahead. Machine learning is the key to accomplishing this. 

What exactly is machine learning

Machine learning is a part of artificial intelligence (AI) that uses data and algorithms to make decisions based on the data while automatically improving through experience. Machine learning has already been present in our life for a while. Just take Google for an example. It is an essential part of all of its services such as Search, Gmail, Maps, Google Assistant, and it is even responsible for ads we see. From organizing your Inbox by separating Important emails from Spam to finding out what is the weather today or which movie is showing in your closest cinema, that is all made possible by machine learning. Google even uses machine learning to prevent illegal fishing.

How to use machine learning for fraud prevention?

In fraud prevention machine learning is used to identify and stop fraudulent behavior. It analyzes the historical data to suggest which risk rules you should apply in your business. 

By using the provided data, it can notice specific patterns and red flags in previous fraud attempts and use them to create a risk score that can be applied for every transaction. This means eCommerce businesses wouldn’t have to rely on the same set of rules for every transaction, such as blocking transactions over a certain amount or even users from a specific location. Instead, there would be a value-added to each risk factor, and a decision would be made based on the final result. This allows you to recognize the fraud attempt before it can do any damage and take necessary action to prevent it. This can be either by blocking the transaction completely or asking the user to provide an additional level of verification.

Machine learning can also help you keep your customers happy as it won’t cause any user friction while running its diagnostics. Unlike the manual transaction review that could take days and result in a high number of false declines, you will be able to get results in real-time with machine learning. According to the survey, one in three consumers would stop interacting with a brand they have loved if they had just one bad experience. Can you imagine how unhappy they would be if their legitimate transaction were declined? Using machine learning will reduce the number of false declines, create an easier and quicker customer journey and ensure the guest satisfaction stays intact. 

The most significant benefit of machine learning is that the algorithm is constantly adapting. Not only does that mean that in time it will be even more accurate, but it will also be able to spot any new patterns as they emerge. This allows us to keep up with the fraudsters since machine learning will be able to recognize any new technique when encountered and adjust risk score to include the red flags connected with it. 

Two types of machine learning

There are two main types of machine learning present in fraud prevention: white box and black box machine learning. While they are different in the way they work, both can be efficient tools in fraud prevention. The biggest difference between them is their ability to show you what they are doing. 

Blackbox machine learning: With this type of machine learning, you will receive the result, but you won’t know how the algorithm came to that decision. It is helpful for smaller companies that are happy just receiving results without having to worry about adjusting the rules or going into the details of programming. This is an extremely useful tool in monitoring red flags that might indicate fraudulent behavior 

Whitebox machine learning: With this type of machine learning, every step is transparent, which means you will not only receive the results but also the explanation of how the algorithm came about it. This offers you more flexibility when it comes to adjusting risk scores.

Both of them have their advantages and would be a welcomed addition to your fraud prevention strategy.

The truth is the fraudsters will never stop trying to exploit businesses and individuals for their own gain. This is why proper fraud prevention is imperative, for every business dealing with sensitive data or financial elements. Machine learning can be the difference between becoming a victim of a fraudulent attack or staying safe and thriving. For the best results, you should implement a combination of machine learning and the human element, as it brings the best from both worlds. 

Gergo Varga
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