AI Fraud Prevention Tools for Fintech and E-commerce: Feedzai, Sardine, Forter, Riskified, and Chainalysis

Most businesses measure fraud by what it takes: chargebacks, stolen funds, refunds. Far fewer measure the other cost, which is legitimate customers wrongly turned away. Decline a good order out of caution and that shopper may never come back. Approve a bad one and you eat the loss, the fee, and sometimes a penalty from your payment processor.

Good fraud prevention is not about blocking as much as possible. It is about finding the right balance between the two errors, and AI helps by scoring risk far more finely than a fixed rule can. This guide compares five platforms across fintech and e-commerce, explains how their approaches differ, and shows which numbers to watch when you evaluate them.

The best fraud tool is not the one that blocks the most. It is the one that blocks the right things.

Two Different Worlds: Fintech and E-commerce

Fintech and banking fraud centers on accounts and money movement: fake or stolen identities at signup, account takeover, authorized-push-payment scams, and money laundering. The decision often happens at onboarding and at each transfer.

E-commerce fraud centers on orders: stolen cards, account takeover for reselling, promo abuse, and return fraud. The decision happens at checkout, in seconds, and a wrong decline directly costs a sale.

Some tools serve one world well and the other poorly. Match the product to where your risk actually sits.

Feedzai: Financial Crime for Banks and Payment Providers

Feedzai applies machine learning to fraud and financial-crime detection at banks and payment companies, scoring transactions and customer behavior in real time.

• Best for: financial institutions with large transaction volumes and regulatory obligations.
• Strength: built for the scale and compliance needs of banking.
• Watch for: it is enterprise-oriented, so smaller companies should confirm fit, pricing, and implementation effort.

Sardine: Fraud and Compliance for Fintech

Sardine targets fintechs and crypto and payments companies, combining fraud detection with compliance checks. It leans on signals such as device intelligence and behavioral data alongside transaction information.

• Best for: fintech startups that need fraud and compliance tooling without building it in-house.
• Strength: a fintech-native approach that links onboarding, payments, and monitoring.
• Watch for: confirm which of your specific payment rails and data sources it supports.

Forter: Checkout Decisions for Online Retail

Forter is an e-commerce fraud prevention platform that makes approve-or-decline decisions on orders and also addresses issues such as account takeover and policy abuse.

• Best for: mid-size and large online retailers with meaningful order volume.
• Strength: focus on maximizing approvals of good customers, not just stopping bad ones.
• Watch for: pricing models and financial terms differ across vendors, so understand exactly who bears the cost when fraud slips through.

Riskified: Approval-Focused E-commerce Protection

Riskified also serves online retailers and is associated with a model in which the vendor takes responsibility for certain approved orders that turn out fraudulent, subject to contract terms.

• Best for: merchants who want to shift some fraud liability and push approval rates up.
• Strength: aligning the vendor's incentives with yours on both losses and approvals.
• Watch for: read the terms carefully. Coverage rules, exclusions, and fees determine whether the model actually saves you money.

Chainalysis: Crypto Compliance and Investigation

Chainalysis is a blockchain analytics company. Its tools help exchanges, financial institutions, and investigators trace crypto transactions and assess risk, supporting anti-money-laundering compliance and investigations.

• Best for: crypto businesses and institutions that touch digital assets.
• Strength: visibility into blockchain activity that traditional systems lack.
• Watch for: it addresses a specific slice of financial crime. It is not a general checkout fraud tool.

Quick Comparison

The Numbers That Actually Matter

When you compare vendors, ask for results in these terms and measure them yourself during a pilot:

• Fraud loss rate. Confirmed fraud as a share of volume.
• Chargeback rate. Keep it comfortably below the thresholds your card networks and processor enforce.
• Approval rate. How many legitimate transactions go through.
• False-positive rate. Good customers declined or sent to manual review.
• Manual review rate and time. Analyst hours are a real cost.
• Customer friction. Extra verification steps that lower conversion.

A vendor that lowers fraud but tanks approvals may leave you worse off. Look at the whole picture.

How to Run a Pilot

1. Export history. Gather several months of orders or transactions with known outcomes, including confirmed fraud and confirmed good.
2. Backtest. Ask vendors to score historical data and compare their decisions to what really happened.
3. Run in shadow mode. Let the tool score live traffic without acting, then compare its calls with your current process.
4. Go live on a slice. Route a small share of traffic through it and measure all the metrics above against a control group.
5. Check explainability. Confirm you can see why a decision was made, especially for disputes and regulators.
6. Read the contract. Focus on liability, data ownership, and what happens if you leave.

Common Mistakes

• Optimizing only for fraud loss. Wrongly declined customers are a hidden cost.
• Ignoring rules of the road. Card-network programs, local regulations, and consumer-protection rules shape what you can do.
• No feedback loop. Models improve when you feed back confirmed outcomes. Confirm how chargebacks and disputes flow into the system.
• Treating fraud as static. Fraudsters adapt. Ask how quickly a vendor updates models against new tactics.

Where AI Adds the Most Value

Fixed rules, such as "block orders over a set amount from a new account," are easy to write and easy for fraudsters to learn. Machine learning adds value in a few specific ways:

• Pattern recognition at scale. Spotting combinations of signals no analyst would write a rule for.
• Adaptation. Adjusting as fraud tactics shift, rather than waiting for someone to update a rule.
• Network effects. Some vendors learn from activity across many merchants, which can surface a fraudster the first time you see them.
• Prioritization. Ranking cases so reviewers spend time where it counts.

Frequently Asked Questions

What is the difference between Forter and Riskified?

Both serve online retailers by scoring orders at checkout. They differ in commercial models, integration details, and how they handle liability for fraud on approved orders. Compare contract terms and run a pilot with your own data.

Do I need a specialist tool if my payment processor offers fraud screening?

Built-in screening is a solid starting point, especially for small merchants. Specialist tools tend to add value as volume grows, fraud becomes costly, or your risk profile is unusual. Measure what your current tools miss and wrongly block before adding another layer.

Is Chainalysis a fraud prevention tool?

It is a blockchain analytics and compliance tool. It helps identify risky crypto activity and support investigations, which is relevant to financial crime, but it is not designed for typical e-commerce checkout decisions.

How much does AI fraud prevention cost?

Pricing varies widely, from per-transaction fees to percentage-of-volume models to enterprise contracts. Ask for a total-cost estimate that includes fees, manual review costs, and any liability terms.

Final Verdict

Start from where your risk lives. Banks and payment providers should look at Feedzai. Fintechs needing fraud and compliance together should look at Sardine. Online retailers should compare Forter and Riskified with a shadow-mode pilot and a careful reading of the commercial terms. Crypto businesses should consider Chainalysis for compliance and investigation.

Whichever you pick, judge it by the full set of metrics, not fraud loss alone. Browse every option with community votes in the AIPick Security category, and see how fraud tools fit with identity and threat detection in our guide to AI security tools.