Machine learning fraud detection and prevention: A beginner’s guide

Machine learning fraud detection and prevention

Fraudsters have shown no signs of slowing down. They’re developing new and more complex ways to access business and customer data. So businesses need to ensure their fraud prevention systems are prepared to combat current and evolving fraud tactics.

That’s where machine learning fraud detection comes in. When businesses and fraud analysts can teach computers to teach themselves, they can reap the benefits of fewer manual reviews and increased revenue.

Read on to learn more about machine learning fraud detection, the differences between supervised and unsupervised machine learning, and the benefits of the technology.

What is machine learning fraud detection?

Machine learning is a form of artificial intelligence that uses algorithms and statistical models to learn from and identify patterns in data. For example, using machine learning for fraud detection, data scientists can train computers to teach themselves to look for patterns that indicate risk and trust.

Machine learning fraud detection combines data and engineering to allow businesses to authenticate every interaction along the digital customer journey. For example, let’s say a business wants to assess the risk or trust associated with an interaction.

If the business uses machine learning fraud detection, the solution has taught itself to look for hundreds of risk or trust factors. So it can decide the outcome by accepting, blocking, or challenging the interaction. Thus, with every decision, machine learning fraud detection can continue to improve results without human intervention.

“For fraud detection, there are so many signals a business can look for in any given interaction,” explained Matty Jones, Kount’s data science manager. “They might have a customer’s name, but they have to be able to answer questions about that name. Is it typically associated with good or bad transactions? And that’s what we train machine learning computers to evaluate. If that name has been associated with risk in the past, it’s more likely to be risky in the future.”

For the best card-not-present (CNP) fraud detection, Jones and his team know basic machine learning isn’t enough. So they combine two critical types of machine learning: supervised and unsupervised.

The difference between supervised and unsupervised machine learning for fraud detection and prevention

Supervised vs unsupervised machine learning fraud detection

The biggest difference between supervised and unsupervised machine learning comes down to outcomes and results. Supervised machine learning, in particular, uses outcome-based data sets to detect risk in an interaction. It uses outcomes and experiences from historical interactions to assess risks in current interactions.

Kount’s unsupervised machine learning links identity elements so that the computer can look for patterns indicative of risk. Essentially, it assimilates billions of data points to validate a transaction and the customer’s device, email address, payment method, geolocation, IP proxy, and more.

“If supervised machine learning is based on experience, unsupervised machine learning is based on intuition,” Jones explained. “Unsupervised learning doesn’t require outcomes. So when it comes to fraud detection, this type of machine learning can detect patterns and anomalies within enormous data sets.”

Top benefits of machine learning fraud detection

Machine learning fraud detection empowers e-commerce businesses to access data and make informed decisions to stop fraud before it impacts their bottom line. But that’s not the only benefit it brings to the table.

1. It assesses risk and trust in real time
Human fraud analysts have human limitations. For example, best practices for manual fraud reviews suggest a single fraud analyst can review around 50 transactions per day. But the more interactions an e-commerce business sees, the harder it is to maintain manual reviews.

But machine learning fraud detection can assess hundreds of interactions for risk or trust in real time. And its unsupervised capabilities mean it can fill gaps in human knowledge quickly.

“The reality is there are too many variables and not enough time for even the best analysts to process fully,” Jones explained. “Any criteria a fraud analyst could check is something machine learning can do almost instantaneously, in less than 200 milliseconds.”

How fast is machine learning fraud detection

2. It’s accurate, retroactive, and proactive
Machine learning fraud prevention is not only fast, but it’s accurate too. And businesses can apply the technology retroactively and proactively. Supervised and unsupervised machine learning work in tandem to assess the actual risk and trust of past and current interactions.

“Essentially, machine learning identifies risk factors in one interaction and looks for similar factors in other interactions,” Jone said. “And when you apply machine learning retroactively, you can see the risk data behind previous transactions and apply those insights to current interactions.”

Data scientists train machine learning models on millions of previous interactions, from account creation to sign-in to checkout. So when you apply machine learning, you can see how the technology would have decisioned your previous transactions.

In the end, you can see exactly how many transactions your business declined that it could have approved — and gained revenue from.

3. It can do more than just fraud detection
Machine learning builds out itself, finding risk and trust patterns a human analyst alone couldn’t see. The more time goes on, the more the technology learns which patterns of risk and trust are stronger than others.

While machine learning is great for fraud detection, it can do so much more. It can also reduce friction in customer experiences and help businesses manage and assess risk across new payment and delivery channels.

Let’s say, for example, you want to start accepting mobile payments, and you get a first-time mobile payment from a customer. Machine learning models can use information from previous mobile payments and similar customers, from all across Kount’s Identity Trust Global NetworkTM, to predict payment behavior.

Now, let’s say you enroll that customer into your loyalty program. Again, machine learning can give you more information about that customer’s lifetime value and predict buying habits, so you can better market to them.

Kount’s machine learning fraud prevention and advanced AI can reduce manual reviews by 77%

Kount’s AI digital fraud prevention combines two types of machine learning and aggregates billions of interactions and their outcomes. It weighs the risk of fraud against each customer’s value and helps identify legitimate interactions from fraudulent ones.

The combination of AI and machine learning allows Kount to provide current and historical insights and instantaneously deliver an actionable safety score. Businesses can rely on Kount’s highly predictive Omniscore when decisioning orders.

Typically, the higher the Omniscore, the safer the interaction. In the end, businesses can rely less on manual reviews to assess the interaction’s relative risk and safety. Look at GNC, for example. At a 2020 Digital Protection Summit webinar, Kount and GNC’s data analysts discussed the benefits of machine learning.

At the time, GNC wanted to optimize business policies, accept new card types and payment methods, and offer BOPIS options. Without machine learning fraud prevention, achieving these goals would have required significant resources. But GNC implemented machine learning instead to increase its approval rate by 1.5% and reduce its manual review rate by 77%.

“The versatility and predictive power of machine learning, used alongside Kount’s incredibly deep and rich global network data, provides a truly powerful state-of-the-art solution for our customers,” Jones concluded. “We can stop fraudsters in their tracks — before they cause damage to the business.”

Machine learning fraud detection and prevention OMNISCORE

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