Unfortunately, the cybercrime industry is expected to be worth over $6 trillion by the end of 2021. This means that the risk of fraud is greater than ever, and many businesses will need to adapt in order to survive.
The good news, though, is that real-time machine learning is a powerful tool that merchants can utilize. But, how does it compare to manual review?
In this article, we'll be comparing machine learning to manual review processes to help you identify which will carry the most benefit to your fraud prevention program.
As the name suggests, machine learning will gradually adapt to improve its own efficiency overtime. This is accomplished by analyzing certain behaviors and recognizing patterns among different instances of fraud.
For example, someone operating a compromised account behaves far differently than a typical user. The average individual who intends to make an online purchase will take time to browse through a catalog, retry descriptions, etc.
A fraudulent user will act as quickly as possible or purchase an abnormally large number of products. Additionally, they may ship their order to addresses that aren't associated with the account owner's personal or billing information.
Since the software learns on its own, it improves and identifies suspicious activity at a more efficient rate than a human is able to.
It's also important to note that you have two options for integrating this method: supervised learning vs. unsupervised learning.
Supervised machine learning functions based off on user-inputted information. In this case, it can be thought of as 'learning by example' and can be used to classify certain content or behaviors.
Unsupervised learning, however, will develop solutions on its own and adapt its methods over time.
Despite the utility that machine learning provides for fraud detection, some companies opt for manual review instead.
More often than not, this is often due to a concern for machine learning's accuracy. This is particularly true during the early stages of machine learning implementation, as it hasn't fully developed yet.
To elaborate, some users may not be impressed with how machine learning initially performs. But, they may change their minds as time goes on.
As you may anticipate, though, there are numerous drawbacks associated with relying on manual review.
One of the most prominent is managing higher volumes of fraudulent transactions as a company scales. While handling a few fraudulent orders manually may not be an issue, it becomes impractical to do so for thousands at a time.
In order to accommodate a higher order volume, you’ll need to spend more money on hiring staff or outsourcing this obligation to a separate party. Additionally, it’s only natural for a human's effectiveness to diminish over long work periods.
You’ll also need to ensure that your staff is consistently trained on emerging threats and understands the appropriate course of action to take. Although there's a certain peace of mind that comes from using manual review, it may end up costing you more in the long run in terms of efficiency.
Machine learning provides merchants with an effective method to detect and prevent fraud. In the future, it's worth exploring how you can integrate real-time machine learning into your company's practices. While there are benefits to manual review, real-time machine learning is quickly becoming the standard for effective fraud prevention programs.
If you're actively looking for a new fraud prevention solution for your company, feel free to reach out to us today and see how we can help.