Harnessing the power of machine learning for payment fraud detection

Ecommerce is set to account for 21.8% of global retail sales by 2026.¹ While this shift to digital allows customers to purchase products with more convenience than ever before, it also increases your business's exposure to sophisticated fraud.

A survey found that 80% of organizations feel more susceptible to rising online fraud due to digital advancements.²

As fraudsters employ increasingly advanced tools like artificial intelligence (AI) to conduct their activities, leveraging machine learning has become essential for staying ahead. Here’s how it can revolutionize your business’s approach to payment fraud detection.

What is machine learning and how does it differ from AI in payment fraud detection?

What’s the difference between AI and machine learning? AI is a general term that refers to a computer system’s ability to mimic human cognitive functions like learning and problem-solving. It relies on math and logic to understand new data and make informed decisions based on that information.

Machine learning, on the other hand, is a subset of AI that uses algorithms to scan vast amounts of data for patterns and insights, applying that learning to make increasingly better decisions. It enables programmers to improve the perception, cognition, and decision-making power of a computer system. In our day-to-day lives, it helps us do everything from detecting email spam to anticipating traffic on our commutes.

When it comes to ecommerce, machine learning for fraud detection specifically leverages algorithms to scrutinize transaction data. Using machine learning algorithms for fraud detection allows businesses to detect fraudulent activities and adapt to new threats continually.

Types of machine learning for payment fraud detection

While all types of machine learning models help businesses fight against fraud, some fraud detection models are especially helpful.

Supervised learning

One of the most common ways of using machine learning for payment fraud detection is supervised learning models, which are “trained” to run predictive analysis with historical data tagged as “good” or “bad.”

For example, a credit card company might use supervised learning to review past transaction data tagged accordingly. The model learns to identify patterns that typically represent fraudulent activities, such as unusually large purchases or transactions in rapid succession, improving its ability to flag similar future transactions as suspicious.

While that analysis is typically faster, more accurate, and more cost-effective than human analysis, its success depends on the quality of the data used to train it.

Unsupervised learning

In unsupervised models, fraud detection algorithms process and analyze untagged data to identify patterns of normal buying activity and detect potentially fraudulent anomalies.

In practice, an ecommerce platform might use an unsupervised fraud detection model to analyze purchasing patterns without prior fraud indicators. The model would then identify typical purchasing behavior and flag activities that deviate significantly from this norm, such as a new, unverified account making several high-value purchases in a short period.

The process is fully autonomous and removes humans – and human error – from the equation.

Semi-supervised learning

As its name suggests, semi-supervised machine learning models split the difference between supervised and unsupervised approaches, using a small amount of labeled data along with a larger pool of unlabeled data.

This approach can be particularly useful in situations where obtaining comprehensive labeled data is costly or impractical. For example, a bank may use semi-supervised learning to enhance its fraud detection systems by training models on a limited set of known fraud cases and a larger set of normal transactions. This approach helps better detect subtle signs of fraud that are not well-represented in the smaller fraud dataset.

Reinforcement learning

Reinforcement learning involves training models to make a sequence of decisions by rewarding them for beneficial behaviors and penalizing them for undesirable ones.

In the context of fraud detection, financial institutions can use reinforcement learning to adjust fraud detection parameters dynamically. For example, if a model lowers a fraud alert threshold and successfully prevents a fraudulent transaction, the model receives positive feedback. Conversely, if lowering the threshold results in a false positive that blocks a legitimate transaction, the model is penalized, learning to find a balance that maximizes accuracy in real-time environments.

Machine learning fraud detection examples

The applications of machine learning in payment processing are far-reaching. Without any human intervention, the algorithms can find patterns — or pattern deviations — in huge amounts of historical data.

This can help you in a number of ways: it enables you to identify customers you may be at risk of losing and act quickly to retain them; build dynamic models that better segment delinquent customers and improve collection strategies and on-time payment rates; and utilize insights from customer payment behaviors to optimize pricing strategies, boosting both profitability and customer satisfaction.

Additionally, machine learning may be a good defense against increasingly sophisticated bad actors, helping with credit card fraud prevention, account takeover detection, and new account fraud prevention.

Signup fraud

Signup fraud occurs when scammers create a new bank or credit card account with a stolen or synthetic identity. Because there’s not much historical data, it can seem inconspicuous to the human eye, but machine learning can analyze third-party data like email addresses, session data, and enrollment data to help spot synthetic identity fraud activity.

Login fraud

Login fraud involves taking over an existing customer account, usually through a stolen login. Machine learning can monitor device, email, IP, phone, transaction, and behavioral user data and rapidly assess whether an individual is a legitimate customer.

Payment fraud

Payment fraud occurs when scammers use credit card details without the real cardholder’s knowledge. Machine learning can quickly analyze previous transaction data and identify anomalies that may indicate fraud.

4 benefits of machine learning in payment fraud detection

The ability of machine learning to rapidly analyze vast amounts of data, identify patterns, and detect anomalies has catapulted it into many facets of everyday life.

By uncovering and mitigating some of the most common types of fraud, machine learning can help enterprises unlock these benefits.

Assess customer behavior in real-time

In the world of cybersecurity, things happen fast. Machine learning algorithms run hundreds of thousands of queries in milliseconds and can often assess individual customer behaviors in real-time. This makes it possible to quickly differentiate legitimate customers from fraudulent ones, helping you quickly approve authentic transactions and create a seamless experience for trusted customers.

Leverage large amounts of data

Effective machine learning is powered by robust, proprietary data. The more data machine learning models have to work with, the better they can distinguish between normal and fraudulent behavior. That may make it a good fit for enterprises that process hundreds of thousands or even millions of monthly transactions.

It doesn’t have to stop with your own data. Shared intelligence could make machine learning algorithms for payment fraud detection strong: PayPal’s two-sided network, for example, is a rich source of transaction and risk data that may help enhance fraud detection.

Adapt to changing fraud patterns

Payment processors aren’t the only ones employing automated technologies to advance their goals. Fraudsters are, too — and advanced tools enable them to find your weaknesses and hide their domains, devices, and even IP addresses. The rules-based systems that have traditionally helped mitigate fraud sometimes can’t keep pace.

With machine learning, you can process large datasets with multiple variables and quickly uncover correlations that might indicate more sophisticated fraud attempts. These models not only identify trends and patterns that human eyes might miss, but they can also continuously adapt to the ever-changing fraud landscape.

Optimize filters and rules

Many types of fraud tools, including rules-based solutions, use filters. Unfortunately, scammers constantly test filters and design new attacks to circumvent them. Machine learning can provide deeper insights to help you customize those filters and rules.

A combination of machine learning and rules-based models can be especially effective. You could use machine learning algorithms to suggest new rules for analysts to create. Alternatively, rules can help train machine learning systems to see certain patterns, creating a layered detection system that relies on both known rules and adaptive machine learning.

The future of fraud detection

In one survey, 58% of respondents cited machine learning as one of the most frequently used technology to detect online fraud.²

This should come as no surprise — payment fraud detection and machine learning go hand-in-hand, helping businesses improve customer satisfaction and lower costs.² As ecommerce continues to grow, machine learning and other emerging AI technologies will likely continue to play larger roles in payment fraud mitigation.

PayPal can help take your business into the future. Explore Fraud Protection Advanced to see how we leverage machine learning, automation, and our 20+ years of experience building risk models to help you streamline fraud analysis, improve decisions, and positively impact your bottom line.