The benefits of machine learning In fraud prevention

Bad actors never sit still. One day it’s stolen card credentials, the next it’s bots hoarding inventory or customers exploiting return policies. With fraud tactics in constant flux, merchants face a huge challenge: how to stop abuse without disrupting the experience for genuine customers.

This is where machine learning (ML) benefits fraud prevention most: It helps merchants strike the perfect balance between protection and customer experience. Let’s take a closer look.

What is machine learning in fraud prevention?

Machine learning in fraud prevention uses data-trained models to decide whether or not an order can be trusted in real time. Unlike static rules that apply conditions based on historical experience, ML evaluates the full context of a transaction, learns from outcomes and adapts to new threats as fraud tactics change.

There are a few different ways ML “learns” from data. The three most common are supervised learning, unsupervised learning and reinforcement learning. The three are often used in conjunction with each other to provide commerce protection.

Supervised learning

With supervised learning, models train on labeled outcomes such as “fraudulent order” vs. “legitimate order.” If past data shows that expedited shipping to mismatched billing addresses often led to chargebacks, the model assigns higher risk to new orders that show the same pattern. This becomes part of a risk assessment that includes hundreds of other conditions.

Unsupervised learning

Unlike supervised learning, unsupervised learning does not rely on labeled outcomes. Instead, it looks for clusters and anomalies in the data itself. That makes it especially useful for spotting emerging patterns. For example, if an account suddenly starts buying gift cards in unusual amounts without a similar purchase history, the model flags that behavior for closer review.

Reinforcement learning

Reinforcement learning improves through trial and error. In fraud prevention, the model refines its strategy by observing which choices lead to better long‑term outcomes. For example, if declining too many orders with mixed signals causes false declines (and by extension causes loyal customers to be turned away), the system adjusts and learns to safely approve more of those transactions.

How exactly is machine learning used in fraud prevention?

Fraud programs often combine multiple models — called ensemble learning — to strengthen decision-making. By weighing different signals like location, payment history, device fingerprints, customer behavior and prior transactions, ML assesses risk in real time with better accuracy.

Here are some of the key ways ML puts those signals to work as a part of a modern fraud management stack:

Real-time behavior and anomaly detection

Flags sudden spikes or unusual activity that falls outside a shopper’s normal pattern, i.e. multiple high-value purchase attempts in a short timeframe.

Context-rich risk scoring

Combines dozens of signals into a single score so you can auto‑approve low‑risk transactions, challenge medium‑risk orders and block high-risk ones.

Text analytics

Reads into unstructured data, including:

• Return reasons
• Delivery notes
• Customer service logs

From there, it surfaces patterns that suggest abuse, like repeat claims of “item not received” or “damaged item” from the same customers.

Network analysis

Links identities, devices and accounts across merchants to uncover fraud rings or serial abusers that would be invisible to one merchant alone.

Adaptive feedback loops

Updates models continuously based on outcomes (i.e. chargebacks, approvals, declines and return decisions). Detection adapts almost instantly to new patterns.

Pattern detection across different journeys

Goes beyond checkout to cover the whole story of the customer journey, from account sign-up and promotions to payment and returns.

But the real value for merchants isn’t just in how efficiently ML works — it’s in the business outcomes it creates.

Top nine ways machine learning benefits fraud prevention

From approving more good orders to continuous optimization, here are nine key advantages of using ML in fraud prevention.

1. Better accuracy

ML weighs all available context to distinguish genuine customers from fraudsters. That precision means you don’t have to err on the side of caution and turn away good buyers. In fact, retailers using Signifyd’s ML-backed decisioning confidently approve 5 to 9% more orders that would otherwise have been falsely declined out of fear of fraud or abuse.

2. Protects loyal customers from false declines

False declines don’t just frustrate shoppers — they siphon revenue. A study by 451 Research estimates false declines cost online retailers $16 billion each year. What’s behind that number? Real customers who wanted to make an honest purchase but were mistakenly rejected.

Since ML models consider context rather than single red flags, fewer good customers get blocked so more honest purchases glide through checkout.

3. A smoother customer experience

Shoppers today expect fast, frictionless experiences. Because ML can accurately approve the vast majority of orders in a fraction of a second, most shoppers never feel the impact of fraud controls. That invisible protection helps preserve the kind of seamless experiences that drive repeat purchases and improve customer lifetime value (CLTV).

4. Continuous protection in real time

As we know, fraud tactics change quickly. Signifyd’s Fraud Pressure Index, which tracks orders arriving with enough red flags to be presumed fraudulent, rose 19% between 2023 and 2024. At the same time, malicious automation is on the rise, with a growing share of attacks carried out by bots running credential stuffing, price scraping and inventory hoarding schemes.

Because ML draws on such a wide range of signals, models can recalibrate to these new threats as they emerge, so your business stays protected without teams needing to scramble to  react.

5. Coverage across fraud and abuse

Fraud can surface at any point in the customer journey, from account creation to post-purchase claims. ML closes those gaps by connecting the dots across transactions and merchants. That means spotting serial returners, promo misuse, return policy abuse or unauthorized reseller activity. And when combined with Signifyd’s global commerce network, models also leverage identities and intent across thousands of merchants worldwide, providing fraud protection at every touchpoint.

6. Automation beyond fraud checks

ML does more than approve or block orders. It also orchestrates downstream workflows. For example, it can automatically route questionable returns for closer inspection or generate complete evidence packages for chargeback representment.

7. Lower costs, fewer manual reviews

Manual reviews are one of the biggest hidden costs in fraud management. They eat up analyst time, slow down checkout and balloon operating expenses. In fact, once you factor in investigations and labor, merchants in North America spend upwards of $4 for every $1 lost to fraud.

ML automates clear approvals and declines, shrinking review queues so analysts can focus on genuinely complex cases while honest shoppers move through checkout faster.

8. Grows alongside your business

From peak holiday traffic to entering new markets, ML scales automatically with demand. It processes thousands of signals in real time so decisions stay fast and precise even when order volumes spike.

9. Continuous optimization

Every decision teaches the system something new, making its fraud prevention capabilities more effective with each transaction. The result is fewer manual reviews, higher approval rates and less revenue lost to fraud.