Why machine learning isn’t just hype: A real-world view from the frontlines of fraud prevention

It’s a common refrain in boardrooms today: Companies should start using artificial intelligence (AI) and machine learning (ML) to make their processes more efficient. This applies to the important work of payment fraud prevention, along with an endless list of other high-priority activities.

There’s a problem, though: The value of these systems isn’t always apparent at first glance. Your company may have a mandate to use AI or ML. But it’s very possible that you’ll fail to find the real value in these systems if you become too caught up in misrepresentations of the technology’s nature or true capabilities.

In fraud prevention, ML can have an especially large impact on the success of illegitimate activity detection. To achieve these advantages, it’s important to inquire about the true nature of modern ML, separating fact from fiction and determining the cases where your brand has the most pressing and immediate use cases.

Machine learning and fraud prevention: What’s at stake

The first question in applying any new technology to a problem is “why?” What problem is pressing enough to demand the application of powerful ML technology, and how does the solution help?

In e-commerce, the reason why you need advanced solutions for fraud prevention is simple, if a little disheartening, because there’s simply so much fraud risk today. With stolen credit card information and identity details available for easy purchase on the dark web and easy-to-use “fraud-as-a-service” products emboldening criminals, there are a host of ways to defraud your business.

In addition to these brute-force fraud tactics, there’s a rising subset of fraud known as first-party misuse, where individuals target your business out of buyer’s remorse or a simple desire to obtain free goods. With such a diverse risk landscape, you need more powerful countermeasures that can keep up.

The ML algorithms used in fraud detection take advantage of big data resources, from your company and your industry in general, to provide indications of fraud. These signals are faster and subtler than ones that came from older, less sophisticated analytical tools.

The volume and velocity of fraud have both become impossible to manage with manual methods. This is why detection via ML-based models has become such a cornerstone of fraud prevention. It would take excessive time and manual effort from your people to provide even a fraction of the performance from an ML-based solution.

The types of machine learning: What they mean, how they help you fight fraud

Just as ML is a separate concept from the monolithic name “AI,” there are several varieties within the ML umbrella. Understanding what these subsets mean will allow you to take stock of what your fraud prevention platforms are achieving for your business.

Categories of machine learning include:

• Supervised ML: This kind of algorithm works based on specific examples. Programmers feed it the exact details of what to look for. In a fraud prevention context, this means examples of fraudulent and legitimate card-not-present transactions. The system then searches for new incoming transactions that conform to the context it has been given, and it will allow or disallow them based on how well they fit the pattern.
• Unsupervised ML: As opposed to supervised ML, unsupervised algorithms are more open-ended. Rather than being explicitly shown good and bad transactions, the systems ingest large volumes of transactions and sort them into groups. This helps find outliers, transactions that don’t fit in with the standard, and legitimate interactions companies have every day. Those outliers can be set aside for closer consideration, and any observed patterns can lead to the development of new fraud detection signals.
• Natural language processing: Another application of ML principles comes in the use of natural language processing (NLP) to recognize patterns in written text. For example, these methods can highlight when communications such as product reviews have been falsified, giving advance warning and helping keep a merchant’s storefront free from fraudulent interactions.

Defending your business effectively means putting ML to use. Fortunately, the current generation of fraud detection technology incorporates multiple algorithms to deliver high-performance capabilities.

Machine learning and AI: Fact vs. Fiction

As with any hyped technology, AI in general and ML specifically can be subject to misapprehensions and myths, which can obscure the systems’ real value. By establishing the true nature and capabilities of ML, you can refocus and pursue its actual potential. Important clarifications include:

• ML is not new but has existed for approximately 30 years in some form. The past 10 years have, however, included a heavy focus on developing powerful new capabilities that have made the systems more immediately useful.
• ML requires clean data. Rather than feeding an undifferentiated stream of information into an ML algorithm, it’s better to curate a well-crafted data set. This focus on data quality allows you to achieve more precise, meaningful results.
• ML doesn’t learn like a living thing. The “learning” in “machine learning” doesn’t mean the systems can make human-like decisions about how to develop or improve themselves. ML is structured in its processes, even when compared to other AI products like generative AI agents.
• ML hasn’t led to human-free fraud prevention workflows. Human judgment still reigns supreme, and people are key when it comes to considering edge cases and ambiguous results identified by ML algorithms.

Start fighting fraud with ML today

ML feels like it comes from the future, but it’s making an impact in the here and now. With years or even decades to incorporate these capabilities into fraud prevention platforms, tech companies have given businesses a powerful weapon against today’s escalating fraud volumes.

The fact that ML is not coming — it’s already here — means that you can and should consider how it can help your business achieve the fraud prevention results you need to cope with today’s rising volumes.

Request a demo to see what the Accertify fraud prevention platform, infused with ML, can do for your business — start moving at the speed of right.