Reviewed by Mayer Hyman, Payments Specialist | Reviewed for accuracy July 1, 2026
Key Takeaways
- Fraud management comes in two flavors: rule-based systems (fast to build, easy to evade) and machine-learning systems (harder to game, better at cutting false declines).
- The strongest chargeback management solutions combine both, not one or the other.
- Industry estimates from LexisNexis Risk Solutions put the true cost of fraud at $3.75 for every $1 lost once chargebacks, fees, and reissued goods are counted.
- The right systems provider question isn’t “do they stop fraud?” It’s “how fast, how cheap, and how compliant?”
You’re an entrepreneur with a growing eCommerce business. You need a payments system that protects revenue without turning every transaction into a fraud investigation. That should be simple. Then you call a chargeback management solution provider, and the conversation turns into a lecture on decision trees, random forests, and neural networks.
What are those, and do you actually need to know?
Here’s the short version: selecting the right chargeback management solution comes down to understanding two technology approaches, knowing what a comprehensive system looks like, and asking the right questions before you sign a contract. This guide covers all three.
What Are the Options for Fraud Management?
Fraud management software falls into two categories: rule-based systems and machine-learning systems. Most merchants don’t need a computer science degree to choose between them, just a clear picture of what each one actually does.
Rule-Based Fraud Management Systems
Rule-based systems run on logic that fraud analysts write by hand: if a transaction matches pattern X, flag it. Software engineers turn those rules into code, and the system applies them consistently.
The problem is that rules only catch what someone already thought to write down. Fraud tactics shift faster than most teams can update their rule sets, and the legacy infrastructure many rule-based systems run on struggles to process modern transaction volumes in real time.
Machine Learning-Based Fraud Detection Systems
According to Altexsoft, a software R&D firm, rule-based systems still dominate the fraud detection market by volume, but systems built on machine learning outperform them on speed and accuracy. Machine-learning models process far more transaction data, and they do it instantly.
Mastercard and Visa both build machine learning and AI into their fraud stacks, tracking transaction size, location, time of day, device fingerprint, and purchase pattern simultaneously. Fraud-focused vendors like Feedzai report similar results: Altexsoft’s roundup of the space cites well-tuned machine learning models catching up to 95 percent of fraud attempts. Capgemini has separately estimated (2017 figures, since the space has moved fast) that machine learning and analytics can cut fraud investigation time by 70 percent while improving detection accuracy by 90 percent.
False declines remain the quiet cost of any fraud system, rule-based or not. Machine learning’s real edge isn’t just catching more fraud. It’s rejecting fewer good customers while doing it.
For more on this, read “Machine Learning 101 — What Merchants Need to Know About Fraud Protection“
Combining Rules and Machine Learning
Rule-based and machine-learning systems aren’t an either/or choice. The strongest chargeback management solutions run both.
Databricks, an enterprise software company, has pointed out that a pure rules-based approach is expensive to maintain: analysts have to keep writing new rules just to stay level with fraudsters, and every gap between rule updates is a window for undetected fraud. Rules also can’t weigh risk tolerance. They’re binary. Machine learning can’t fully replace that judgment either, but pairing the two, the speed and scale of machine learning with the clear-cut logic of rules, gets closer to complete coverage than either one alone.
What Makes Fraud Management Software Actually Effective?
The best chargeback management solutions share five traits: comprehensive coverage, layered protection, easy integration, security compliance, and mobile support. A provider missing more than one of these is worth a second look before you sign.
Comprehensive coverage. Alexey Konyaev, head of fraud for SAS, has argued that a system built to catch only one fraud type isn’t efficient enough to matter. It needs to process every data type at scale, not just the obvious ones.
Multiple protection layers. One layer should handle user authentication, device fingerprinting, and transaction geolocation. A second layer should watch for anomalies in ongoing customer behavior, since fraud patterns often show up in the account, not just the transaction.
Easy to deploy and integrate. A chargeback management solution should connect cleanly with your existing systems, not force a rebuild. Before signing, check reviews on Gartner Peer Insights, G2Crowd, Capterra, and FinancesOnline to see how the integration actually goes for other merchants.
Security compliance built in. Any merchant processing card payments is expected to meet PCI DSS. That’s not a legal mandate, but card brands can fine noncompliant merchants up to $500,000 and revoke their ability to accept payments entirely, which functionally shuts the business down.
3-D Secure on the roadmap, if not already live. 3-D Secure adds a PIN-based second layer of authentication for online purchases. Many merchants now treat it as a non-negotiable line item for preventing chargebacks before they happen, not an optional add-on.
Mobile fraud coverage, not a desktop afterthought. An eCommerce site without solid mobile fraud coverage is leaving revenue exposed. Mobile commerce has grown from a niche channel to a majority share of retail transactions in most verticals, and any fraud system built for desktop-first traffic will miss what mobile-specific fraud looks like.
Providers like Feedzai, NoFraud, Signifyd, iovation, SAS, SAP Business Integrity Screening, and Cartis’s own proprietary chargeback management tools all integrate with platforms like Shopify, Magento, BigCommerce, and Salesforce Commerce Cloud through API.
Related: “Customers Are Now the Biggest Fraud Threat to Merchants — How to Fight Friendly Fraud“
What Does a Chargeback Actually Cost You?
Chargebacks cost more than the disputed transaction. Once you count the transaction reversal, the chargeback fee, the lost goods or services, and the labor to fight the dispute, LexisNexis Risk Solutions’ annual True Cost of Fraud study has put the multiplier at roughly $3.75 in total cost for every $1 of fraudulent transaction value for card-not-present merchants. On volume, that adds up fast, and it’s a cost most merchants only see after the fact.
That’s the real argument for prevention over dispute-fighting: a chargeback management solution that stops the fraudulent transaction before it happens is cheaper than the best dispute-response process in the world.
Best Practices Checklist Before You Choose a Provider
Merchants can run fraud management in-house, but it takes a dedicated team of rule-based and machine-learning specialists to do it well. Most merchants outsource instead, choosing a payments provider that integrates fraud management directly into the payment stack.
Before comparing providers, run through this checklist:
- Does it combine rule-based and machine-learning technologies, or just one?
- Setup and integration timeline and cost
- Compliance with your specific industry’s requirements
- How comprehensive is the underlying data the solution actually draws on?
- Clear, trackable KPIs, not vague “improved fraud protection” promises
- Will they share real performance data, or only marketing claims?
- Average technical support response time
- References from real clients in your industry, not just case study logos
Combining Payments Architecture With Fraud Management
Cartis Payments is a payment processing provider, not a bolt-on fraud vendor, which is why its chargeback and fraud prevention tools ship built into the payment stack instead of layered on top of it. As an Elavon payments software integration partner working with ISVs, B2Bs, and merchants of every size, Cartis pairs Elavon’s merchant services, payment gateways, and card processing infrastructure with its own proprietary fraud and dispute tools. That combination represents a working relationship between merchants, card issuers, acquirers, and cardholders, protecting transactions and keeping customer data secure at every step. Contact Cartis to talk through your current payments and fraud setup.
FAQ
What’s the difference between rule-based and machine-learning fraud detection?
Rule-based systems apply fixed logic written by fraud analysts. Machine-learning systems analyze transaction patterns in real time and adapt as fraud tactics change. The strongest chargeback management solutions combine both rather than relying on either alone.
How much does a chargeback actually cost a merchant?
LexisNexis Risk Solutions’ True Cost of Fraud study estimates the true cost at around $3.75 for every $1 of fraudulent transaction value, once chargeback fees, lost goods, and labor are included.
Do I need 3-D Secure if I already have a fraud management platform?
Most merchants treat 3-D Secure as complementary, not redundant. It adds a second authentication layer for online purchases specifically, which reduces chargebacks that a backend fraud platform alone might not catch until after the sale.
Should I build fraud management in-house or outsource it?
In-house requires a dedicated team of rule-based and machine-learning specialists, which is realistic mainly for high-volume merchants. Most merchants outsource to a payments provider with fraud management built into the processing stack.
