Best Fraud Detection Software (2026): Top 11 Compared

Stripe Radar is the fraud detection product built into Stripe's payments platform. Stripe's documentation explains that Radar evaluates transactions in real time and that every pricing tier charges for each transaction it evaluates, including recurring payments, although Stripe Billing users pay only for the first transaction in a subscription. The pricing page states that Radar's AI works whether or not Stripe processes a business's payments, but protection for payments outside Stripe sits under the enterprise option.

Radar Lite covers AI-based fraud prevention for card payments, card testing prevention and fraud alerts at no extra charge on standard Stripe payments pricing. For individual businesses, Radar Standard starts at $10 a month, Plus at $14 and Pro at $20, each with a pay-as-you-go alternative. Platform tiers start at $20, $44 and $70 a month, and enterprise pricing is custom. These US dollar figures were last read in September 2026; in October 2026 the pricing page served regional prices to this research.

Standard covers all payment methods, default rules, allowlists and blocklists and a three-level risk rating. Plus adds 0 to 99 risk scores, custom rules with backtesting, manual reviews, Adaptive 3D Secure and Radar Assistant. Pro adds dynamic risk thresholds and abuse signals for free trials, bots, multiple accounts and account sharing.

Existing users face a billing change. A Stripe support article read in October 2026 says accounts on Radar before 29 July 2026 received a free trial of Radar Standard and then continue on Standard automatically, while Radar for Fraud Teams users move to Plus. New pricing applies from 22 January 2027, and any account can switch to Radar Lite from the Dashboard. The clearest fit is a merchant or platform already processing cards on Stripe.

SEON is a fraud prevention platform that has extended into AML compliance, and it also appears on the StatWharf AML software page. This entry covers the fraud line. The pricing page describes an API-first product meant to be connected quickly and says it draws on more than 1,000 online and device signals to identify risky users and transactions. Customer quotes on that page cite payment fraud reduction, bonus abuse prevention and multi-accounting detection; those results are vendor-reported.

Pricing is unusually transparent for the category. Starter costs $699 a month and includes platform and API access, 2,500 fraud checks a month, 10 users and 50 custom rules, with support by ticket and email. Premium is priced on request and adds case management, AML compliance, unlimited API calls, users and custom rules, a dedicated implementation team, round-the-clock support and Managed Risk Services.

The two tiers give smaller online businesses a practical path. A merchant, marketplace or gaming operator can start on Starter with a fixed budget, then move to Premium when volumes grow or compliance work requires case management. Buyers should ask what checks beyond the Starter allowance cost, because no overage rate is published. SEON sells detection and tooling, not chargeback liability cover, and banks with large monitoring estates are not the audience the pricing page describes.

Sift describes itself as a fraud prevention platform for digital businesses that combines machine learning models, configurable workflows, identity data and expert analysts. Its homepage cites a data network of more than 1 trillion annual events and names Hertz, Yelp and Poshmark among its customers. Coverage spans payment fraud, account takeover and account abuse.

Payment Protection is the product most relevant to transaction fraud. Sift states that each transaction is scored in under 150 milliseconds from device, behaviour, network and identity signals, with more than 16,000 risk signals behind each score. The vendor describes a patented ensemble of continuously retrained models plus global, cohort and custom models. Listed fraud types include stolen cards, card testing, buy-online-pick-up-in-store abuse and alternative payment method fraud.

Control is the main theme. Sift points to Score Explainability, network visualisation and transaction views so analysts can see why a decision was made. Teams can test policies against historical traffic before release, build workflows in a no-code decision engine and act in bulk. The vendor also lists chargeback data import, PSD2 compatibility and integrations with identity verification, 3D Secure, KYC and AML providers.

Sift publishes no prices, and its pricing path returns no page. The product suits a fraud or trust and safety team that wants to tune decisions itself. Merchants that prefer to hand fraud losses to a vendor may look at the guarantee-based providers on this page.

Signifyd sells ecommerce fraud protection built around a financial guarantee. Its Guaranteed Fraud Protection product approves or declines each order at checkout, and Signifyd reimburses merchants for fraud chargebacks on approved orders, which moves fraud loss from the merchant to the vendor.

The product page cites more than 25,000 data features and custom, chained models trained for different ecommerce verticals. The pricing page says Signifyd correlates session and transaction data with forensic data and more than 150 out-of-band identity sources. Merchants can tune the risk threshold against target chargeback and approval rates, and the models learn from chargebacks and authorisation outcomes.

The pricing model is stated even though the rate is not. Signifyd charges a percentage of the order total when an order is approved and nothing when an order is declined for fraud. The percentage depends on products sold, vertical, order volume and average ticket, and follows an assessment of current fraud operations. The guarantee is configurable, from fraud-only protection to a total chargeback liability shift.

Signifyd lists pre-built integrations with Miva, BigCommerce, Adobe, Salesforce Commerce Cloud, Shopify, NetSuite and VTEX, payment integrations with Adyen, Stripe, PayPal, Square and Worldpay, and APIs and SDKs for custom builds. The fit is an online retailer that wants a predictable fraud cost per approved order rather than an in-house tuning operation.

Riskified sells ecommerce fraud prevention built around a chargeback guarantee. Its developer documentation states that Riskified stands behind the guarantee for all approved orders, which moves fraud chargeback losses on those orders from the merchant to the vendor. Riskified's main website blocked automated access during the October 2026 check, so this entry relies on the vendor's developer documentation.

The documentation lists five products. Chargeback Guarantee reviews orders before or after payment authorisation and returns an approve or decline decision. Adaptive Checkout recommends when to apply 3D Secure or a CVV check, including PSD2 flows in regulated markets. Policy Protect analyses refund and return claims for abuse. Dispute Resolve handles chargeback disputes, and Account Secure targets account takeover. An OTP Recover flow can send a one-time passcode to a customer after a decline, so an eligible legitimate order can still be approved.

Integration options are documented in detail. Riskified lists platform integrations for Shopify, Magento, VTEX, commercetools, SAP Commerce and Salesforce Commerce Cloud. Its chargeback gateway integrations for Braintree, Adyen, PayPal, Stripe, Shopify Payments and Checkout.com automate chargeback reporting through to dispute. A device and session SDK called Beacon covers web, iOS, Android, React Native and Unity.

No price figures appear in the documentation reviewed, so buyers should expect a quote. The fit is a larger online merchant that wants the vendor to carry chargeback risk on approved orders and also faces refund, return or account abuse. Banks monitoring account transfers sit outside the product's stated scope.

Forter positions itself as a decision platform for commerce, covering fraud management, payment optimisation and what it calls agentic orchestration. Its homepage stresses that the product is payment and processor agnostic, so merchants are not tied to one acquirer or gateway.

The fraud management product rests on the Forter Network, which the vendor sizes at more than 2 billion identities across more than 250,000 storefronts. Forter says its AI analyses identity, behavioural, device and payment signals in real time to recognise returning legitimate customers and stop chargebacks. The same decisions apply across cards, wallets, buy now pay later, ACH, gift cards and alternative payment methods. For investigations, an Analytics Agent assembles transaction context and surfaces the relevant signals.

Two areas extend beyond classic fraud detection. Payment Optimization aims to raise authorisation rates by orchestrating payment flows. Agentic Orchestration addresses AI shopping agents, which the vendor says lets brands accept legitimate AI shoppers while blocking automated attacks. Buyers preparing for agent-initiated purchases may find this relevant; others can treat it as optional scope.

Outcome averages and customer quotes on the homepage are vendor-reported. Forter publishes no prices, and its pricing page is a contact-sales form. The fit is a large online merchant, marketplace or travel business with several payment providers and enough volume to justify an enterprise contract.

Sardine describes itself as an agentic risk platform that unifies fraud prevention, AML compliance and real-time transaction monitoring for banks, merchants and fintechs. It is one of the few vendors on this page with separate product pages for merchant payment fraud and bank payment fraud.

For merchants, the payment fraud page describes pre-authorisation risk scores from checkout behaviour, device risk, shipping address signals and cardholder verification. Rules can be built and backtested without code, and workflows can approve, step up, decline or route orders to a chargeback guarantee provider. An AI agent reviews disputes and gathers evidence. Coverage extends to gift cards, digital goods, and deposit and cashout risk in digital wallets and iGaming.

For banks and fintechs, the bank payment page lists ACH, wires, SEPA, RTP, FedNow, Zelle and checks. Sardine says it screens payments before funds move, can hold instant payments for review and predicts ACH returns by return code.

The modelling layer is open. Sardine offers consortium-trained models, a feature store of more than 12,000 risk features for training custom models, and hosting for models a team brings itself. The platform also covers KYC, KYB, document verification and sanctions screening. No prices are published, so buyers should ask how cost scales across products. The fit is a fintech, sponsor bank or payments company moving money across several rails.

Feedzai is an AI-native fraud and financial crime prevention platform serving customers from global banks to fintechs. Its homepage states that it secures $19 trillion in payments and processes 140 billion events each year, and reports a tier 1 bank detecting more fraud than with its previous solution. These are vendor-reported figures. The site also cites a leader placement in the Chartis 2026 RiskTech Quadrant for fraud platforms.

The Transaction Fraud solution combines behavioural, non-monetary and monetary data to detect fraud across every payment channel. Its 360-View Customer Profiles build a risk profile for each customer, an approach the vendor calls segment-of-one profiling. TrustScore adds network-wide intelligence from Feedzai's customer base, and the vendor states that clients detect four times more fraud with half the alerts compared with rules alone. Explainable AI and integrated case management are part of the product.

Feedzai says the platform ingests data from existing transaction systems and third-party tools, accepts custom rules and external scores, and supports importing and exporting machine learning models. Behavioural biometrics, device intelligence and a separate AML capability are offered on the same platform.

No prices are published. The fit is a retail bank, card issuer or processor that needs real-time scoring across many channels and has the model governance to run a configurable platform. Ecommerce merchants seeking a checkout plugin will find the guarantee-based vendors closer to their needs.

Featurespace builds AI tools that help financial institutions prevent fraud and financial crime. Visa announced on 19 December 2024 that it had completed its acquisition of Featurespace, which joined Visa's Risk and Identity Solutions unit, with its products to be incorporated into Visa's offering. Buyers should ask how the product is packaged and sold today.

The Featurespace Platform is built around Adaptive Behavioral Analytics and Automated Deep Behavioral Networks, machine learning techniques that the vendor says profile each customer's actions, compare them with peer groups and analyse scam behaviour as it changes. The homepage says the platform is used by banks, payment processors, merchant acquirers, insurers and gaming organisations, and is deployed on premises or as a fully hosted cloud service in more than 180 countries.

The solutions page adds risk scoring for scams and account takeover and a multi-tenancy capability that lets issuers and acquirers control the experience offered to their own clients, which suits processors running fraud detection for many institutions. Featurespace publishes case studies with NatWest on scam detection and TSYS on fraud losses; those results are vendor-reported.

No prices are published. The fit is a bank, issuer, acquirer or processor with in-house fraud strategy teams and a need for real-time behavioural scoring, particularly for authorised push payment scams. Online merchants are not the buyer the vendor's pages describe.

NICE Actimize is the financial crime division of NICE and also appears on the StatWharf AML software page. This entry covers its fraud products. The Enterprise Fraud Management page presents IFM as an AI-driven platform that applies AI across detection, strategy, investigations and operations, with intent-based AI, granular customer profiles and collective industry intelligence across channels and payment types.

IFM is organised by use case. Scams and Mule Defense looks for suspicious patterns across the customer lifecycle, including money mule accounts. Payments Fraud provides real-time payment monitoring, and New Account Fraud targets onboarding. Investigations brings investigation functions and data into one platform, and Authentication Management helps institutions manage customer authentication across channels. The page's resources cover check fraud, fast payments fraud and authorised push payment scams.

Smaller institutions have a separate route. The Xceed page presents Xceed AI FRAML as fraud prevention and AML compliance on one platform for credit unions and community banks. It has three parts: Xceed AI Fraud for transaction monitoring, Xceed AI AML, and Xceed AI Investigations, which uses AI agents to triage routine investigation work.

No prices are published, and both product pages lead to demo requests. The fit is a bank or credit union with an established fraud function, especially one consolidating fraud and AML operations. Online merchants and early-stage fintechs will find lighter, API-first options elsewhere on this page.

SAS Fraud Decisioning is a cloud-native fraud detection and prevention product on SAS Viya, the vendor's analytics platform. SAS describes it as a tool for financial institutions to detect, prevent and investigate fraud in real time across payments, account onboarding, customer authentication and digital commerce. A separate StatWharf page on fraud detection software for banks covers it alongside other bank-focused vendors.

The product page lists a wide range of use cases. They include payment fraud and payment scams involving social engineering, money mule and funnel accounts, account takeover, synthetic identity and application fraud, bust-out fraud on new credit applications, check fraud with check image analysis, and ecommerce fraud across merchants and third-party processors. SAS states that the product profiles, scores and evaluates every transaction in real time with millisecond response times.

Strategy tooling is the main distinction. Fraud teams combine business rules, anomaly detection, machine learning models and prebuilt fraud models, test changes with champion-challenger methods, and can write detection rules in natural language through SAS Viya Copilot. Model management and governance, alert triage, case management and reporting sit on the same platform, which matters at banks where fraud models face model risk review. SAS also cites analyst recognition from Forrester and Chartis in 2026.

No price is published. The fit is a bank, card issuer or payment provider with data science staff that wants to build and govern its own fraud models next to prebuilt ones. Merchants seeking a checkout integration will find the ecommerce vendors on this page closer to their needs.