Rules-based vs. machine learning in fraud protection

Every online order carries a question: Is this a trusted customer or a fraudster in disguise? For merchants, getting that call wrong can mean less revenue, more chargeback headaches and a hit to the trust you’ve built with loyal customers.

That’s why the way you approach fraud protection matters just as much as the decision to have it. For many retailers, the choice often comes down to rules-based vs. machine learning (ML) fraud protection. Each system tackles fraud in a very different way, and knowing where they excel (and where they fall short) is key to protecting revenue while preserving the customer experience.

What is rules-based fraud protection?

Rules-based fraud protection is a system that uses pre-set, human-written conditions to decide whether to approve, review or block a transaction. These rules, which can be rewritten and added to as conditions change, are often built from known fraud patterns or business-specific policies, like:

• Blocking orders from certain IP addresses or locations
• Requiring additional verification for high-value purchases
• Flagging transactions with mismatched billing and shipping addresses

Put simply, each decision to approve or deny a transaction comes down to whether an order meets or breaks a defined rule. When conditions arise for which no rule exists, decisions are passed off to manual review teams or consultants are called in to write additional rules.

Advantages of rules-based fraud protection

• Rules-based fraud detection can give merchants who require relatively few rules a sense of clarity and control. With few rules in place, a merchant can often determine which rule or rules factor into a particular ship or don’t-ship decision. Attributing the reason for a decision becomes increasingly difficult as more — and sometimes overlapping — rules are added or as more time passes and the original intent of some rules is lost.
• Some stakeholders — particularly in large, compliance-heavy enterprises — find comfort in rules-based systems’ compliance and reporting options. For example, many large enterprises segregate duties, so that the person who creates a rule cannot also launch it in a production environment. That provides an opportunity for other team members to review the rule and request changes before the rule is implemented.

Disadvantages of rules-based fraud protection

The clarity and control of rules-based systems come at a cost.

• Fraud tactics change daily, and keeping pace means constantly adding, editing and monitoring rules. Keeping up with rules becomes all the more challenging as a business expands, builds new sales channels and launches new initiatives such as buy online, pick up in store (BOPIS); same-day delivery, flash sales and high-value fraud screening.
• Rules-based systems primarily focus on preventing fraud, not fostering growth. The need to constantly add new rules to long-standing rules can result in a tangle of rules that neutralize or even contradict each other.
• Over time, fraud teams can end up spending more energy managing an ever-growing web of rules (sometimes called “rule sprawl”) than on improving systems and building innovative initiatives. That often results in rules that are too lenient and allow sophisticated fraudsters through combined with rules that are too strict and block legitimate shoppers. The rule sprawl often requires merchants to pay for outside consultants who act as rules wranglers.
• The potential for false declines due to static rules can mean that rules-based systems too often rely heavily on manual review, increasing costs, slowing fulfillment and damaging customer experience.
• The damage from those false declines has a ripple effect. According to Signifyd’s research, 27% of consumers who are wrongly turned away never return to that retailer. That means the merchants aren’t just losing a sale, but the full lifetime value of that customer.

What is machine learning fraud protection?

ML fraud protection systems use real-time data, historical data, customer behavior patterns and other signals to adapt to new threats and spot fraud that rigid rules can miss. These signals can include:

• Device fingerprints
• Geolocation
• IP addresses
• Past transaction histories
• Payment methods
• Behavioral clues, i.e. how quickly a shopper moves through checkout

Individually, these signals may not tell the full story. But together, they reveal patterns that indicate whether a transaction is trustworthy. And when combined with data from a global commerce network like Signifyd’s, models also learn from aggregated trends across ecommerce, which helps them spot new patterns even faster and contribute to the many benefits of ML in fraud prevention.

Advantages of ML fraud protection

• Machine learning-powered fraud protection is highly adaptable. Models retrain themselves automatically on new data as it flows in, enabling them to spot fraud patterns as they emerge. Because ML works at scale, it can detect complex patterns that static rules or human review could miss.
• ML systems consider multiple signals at once to decide if a transaction is safe. And with recent advances in model explainability, fraud teams today have more visibility into why decisions to approve or deny transactions are made. 
• Industry leading machine learning protection providers, like Signifyd, align their interests with the merchants they serve. Their guaranteed fraud protection model provides a guarantee on approved orders while charging a small percentage of each successful sale. Such guaranteed models mean the fraud protection provider receives no payment on declined orders. So it is in the best interest of both the merchant and the solution provider to approve as many orders as possible. 
• Many advanced systems also rely on ensembles, or groups of models, each trained on different signals or fraud patterns. By combining their strengths, ensembles improve accuracy and make it harder for fraudsters to exploit weaknesses.

Disadvantages of ML fraud protection

Like any approach, ML-powered fraud protection comes with challenges.

• Models need clean, diverse and high-quality data to perform well. This means smaller or newer merchants may not see immediate value without access to larger data sets, such as those provided by a consortium commerce protection model. 
• While explainability is improving, it’s still a challenge. Unlike rules, which are tied to a clear condition you can sometimes point to, ML models generate probability scores based on patterns across many factors. That process makes them powerful at detecting subtle risks and recognizing unusual, but legitimate orders. It can also mean the reasoning behind a decision isn’t always immediately obvious, depending on the fraud protection provider. Industry leading ML-powered fraud protection systems, like Signifyd, automate order flow based on probability scores and provide accessible insights into why decisions are made.
• For stakeholders used to rigid rule logic, ML fraud detection systems lacking dedicated insights features can feel like a “black box.” And when fraud teams can’t quickly explain why a transaction was approved or declined, it becomes harder to reassure customers, equip support teams with clear answers or give executives confidence in the system’s decisions.

Why does choosing an effective fraud protection system really matter?

Choosing a fraud protection system that uses effective methods lets your business preserve a positive shopping experience while preventing costly fraud.

Without strong safeguards against suspicious activity, you risk lost revenue and damaged trust, not to mention higher chargeback costs. In fact, according to a Mastercard-backed study, fraudulent chargebacks are projected to reach $15 billion globally in 2025. And that’s before even counting legitimate disputes or factoring in newer chargeback fee structures from Visa and Stripe.

That’s why choosing the right system behind your fraud protection strategy, whether it’s rules-based or ML-powered, is so crucial.

What is the difference between Rules-based vs. Machine Learning fraud protection?

Both rules-based and ML-powered approaches help ecommerce merchants fight fraud, but they operate in different ways.

	Rules-based fraud protection	ML-powered fraud protection
Decision-making	Follows fixed “If X, then Y” logic, with decisions being straightforward but often rigid	Weighs many factors to make dynamic, data-informed decisions in real time
Adaptability	Rules must be manually updated to handle new fraud tactics	Continuously learns from data and auto-adjusts to changing patterns
Explainability	Can be easy to explain to stakeholders because decisions can sometimes be tied to a specific rule	Not always intuitive, but many systems have tools to explain decisions
Accuracy and performance	Effective for known fraud patterns but can lead to more false positives	Higher precision with fewer false declines and missed fraud attempts
Scalability	Becomes harder to manage as transaction volume and rule counts grow	Easily scales and handles large volumes without added rule complexity
Impact on customer experience	Strict rules can sometimes trap honest customers in broad filters, leading to frustration	Balances protection with a smoother experience for trusted shoppers
Fraud readiness	Reactive: generally responds after threats appear	Proactive: adapts to emerging tactics

Case study: The evolution from rules-based to ML-powered fraud protection

The right approach to fraud protection often depends on a merchant’s growth stage. While rules-based solutions can be sufficient for small businesses, as businesses grow, rules systems can struggle to remain efficient and cost-effective. That’s where machine learning’s practical value comes in.

Like most retailers, leading apparel and pop culture brand Hot Topic reached the realization that while its brick-and mortar operation was thriving, the time had come to ride the growth of ecommerce. It turned its attention to creating a fantastic online experience for its loyal customers and doubled down on promoting its digital sites. 

At the time, online fraud was under control without the automation provided by machine learning fraud protection. But as its online operation grew wildly, keeping up with the increase in orders began to take its toll. An increasing reliance on manual review slowed order processing, at times by up to a day.

CEO Steve Vranes turned to automation and an ML solution. The 24-hour delay evaporated while at the same time, the number of false declines plummeted. And that improvement turned into serious revenue. With 94% of orders that previously would have been declined, now being approved, Hot Topic realized a multimillion dollar increase in annual revenue.

Most important to Vranes, it also meant Hot Topic customers were enjoying a peerless customer experience.

Machine Learning fraud protection: How is it better?

Where ML outperforms rules 

• Offers scalability for high-volume transactions: ML models handle peak transaction volumes during holidays or product launches without requiring you to add dozens of new rules.
• Improves decisions: ML uses historical, behavioral and transaction data to determine if an order is truly fraudulent, which helps reduce false declines.
• Increases efficiency: By providing an automated ship or don’t-ship decision, ML helps accelerate order flow and fulfillment. Orders that are not clearly either a ship or don’t-ship order can be referred to manual review. The adaptive model reduces the need for constant and cumbersome rule adjustments.
• Enhances anomaly detection: Since ML models are trained to spot subtle combinations across a user’s device, behavior and transaction history, they’re effective at identifying sophisticated fraud schemes like account takeovers (ATO), triangulation fraud and reshipping scams.

Make smarter decisions with machine learning fraud detection

When done well, fraud protection feels seamless for the customer. Good shoppers move through checkout without extra friction, while fraudsters are quietly stopped in the background. Signifyd’s Guaranteed Fraud Protection makes that possible by combining ML, behavioral signals and intelligence from a global commerce network.

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