Best Tools for Card-Not-Present Fraud Detection

Summary

Card-not-present (CNP) fraud is a rapidly growing threat for B2B merchants and SaaS platforms, especially as digital payments and remote transactions become the norm. Navigating this evolving landscape requires more than basic safeguards-businesses need advanced solutions that can adapt to new fraud tactics while maintaining operational efficiency and regulatory compliance.

Selecting the right tool is not just about stopping fraud; it’s about safeguarding revenue, streamlining compliance workflows, and empowering your teams to focus on growth. In this guide, we offer a strategic comparison of leading B2B fraud detection platforms. You’ll find an in-depth, impartial look at each solution’s features, use cases, and unique strengths, so you can make an informed choice that aligns with your business goals as the threat landscape continues to evolve.

1. FraudNet

Platform Summary:
FraudNet is an enterprise-grade, end-to-end fraud detection platform purpose-built for the evolving challenges of card-not-present fraud. Leveraging advanced technology and a global anti-fraud network, FraudNet empowers organizations to proactively isolate fraudulent activity, reduce false positives, and protect revenue while maintaining a frictionless experience for legitimate customers.

Key Benefits:

• Real-time transaction monitoring with sub-300ms decisioning to block suspicious CNP activity before it impacts revenue.
• Adaptive models that uncover subtle anomalies and adapt to new fraud tactics, reducing false positives.
• Access to the world’s largest anti-fraud intelligence network for proactive defense against emerging threats.
• No-code, intelligent risk decisioning engine for agile rule creation and workflow automation without developer resources.

Core Features:

• Real-time scoring and adaptive risk engine for instant, explainable decisions.
• PCI DSS compliance and seamless integration with payment gateways, CRMs, and ERPs.
• Modular, scalable architecture that grows with your business and evolving fraud challenges.
• Streamlined data orchestration to break down silos and unify risk management.

Primary Use Cases:

• E-commerce & retail: Reduce chargebacks and prevent losses from stolen card data while ensuring a smooth customer experience.
• Online marketplaces: Monitor transactions and prevent fraudulent onboarding of merchants and buyers.
• Travel: Detect unusual booking activity and reduce false declines in high-value, complex CNP transactions.

Recent Updates:
In 2024, FraudNet won the Datos Insights Award for its Joint AML and Fraud Transaction Monitoring solution, highlighting its leadership in unified financial crime management. The platform also launched an Entity Screening module to automate business and customer verification, and a Policy Monitoring solution to proactively manage merchant compliance and contractual risks.

Setup Considerations:
FraudNet’s modular design allows organizations to implement only the tools they need, with seamless integration into existing workflows. Powerful data orchestration capabilities enable easy ingestion and enrichment of data from current systems, while the platform’s end-to-end approach ensures comprehensive protection without operational disruption.

2. Stripe Radar

Platform Summary:
Stripe Radar is a machine learning-based fraud detection solution natively integrated into the Stripe payments ecosystem. It leverages data from millions of global businesses to identify and block suspicious CNP transactions in real time, making it a frictionless choice for digital-first organizations.

Core Features:

• Machine learning fraud detection using global network data.
• Custom rules and workflow automation for tailored fraud operations.
• Native integration with Stripe payments for instant activation.
• Device fingerprinting and behavioral analytics for enhanced accuracy.

Primary Use Cases:

• Blocking unauthorized card usage and suspicious sign-ups for SaaS platforms.
• Screening risky buyers and sellers in online marketplaces.
• Supporting global eCommerce with multi-currency and local payment compatibility.

Recent Updates:
Stripe Radar has recently enhanced its machine learning models with device fingerprinting and behavioral analytics, improving its ability to detect and adapt to new fraud tactics.

Setup Considerations:
Stripe Radar is only available to businesses using Stripe as their payment processor. While setup is seamless for Stripe users, organizations with diverse payment stacks may find flexibility limited. Chargeback protection requires additional service agreements.

3. Adyen RevenueProtect

Platform Summary:
Adyen RevenueProtect is a behavioral analytics and machine learning-driven risk engine designed for global B2B and omnichannel retailers. It unifies payment processing and fraud management, reducing operational complexity for multinational merchants.

Core Features:

• Real-time behavioral analytics and machine learning for CNP fraud detection.
• 3D Secure 2.0 support and dynamic, customizable risk rules.
• Unified global acquiring and fraud orchestration.
• Dynamic payment routing and real-time risk scoring.

Primary Use Cases:

• Detecting and adapting to cross-border fraud risks for international B2B payments.
• Protecting digital and physical transactions for omnichannel retailers.
• Ensuring secure recurring payments and SCA/PSD2 compliance.

Recent Updates:
Adyen RevenueProtect recently launched dynamic payment routing and real-time risk scoring for merchants operating in multiple global markets, enhancing both fraud detection and payment efficiency.

Setup Considerations:
Setup and integration can be complex, especially for non-technical teams. Volume minimums may restrict accessibility for smaller businesses, and some advanced features require collaboration with Adyen’s implementation specialists.

4. Sift

Platform Summary:
Sift is a holistic trust and safety platform that combines dynamic risk scoring, device intelligence, and automated chargeback management to address a broad range of fraud and abuse scenarios in digital commerce.

Core Features:

• Real-time dynamic risk scoring and device fingerprinting.
• Automated chargeback management and evidence submission.
• End-to-end trust and safety platform covering payment fraud, account abuse, and content moderation.
• Expanded device intelligence and real-time API endpoints.

Primary Use Cases:

• Preventing fake accounts, payment fraud, and promo abuse in marketplaces.
• Defending SaaS and digital goods businesses against account takeovers and fraudulent purchases.
• Streamlining chargeback dispute workflows and improving win rates.

Recent Updates:
Sift has introduced new API endpoints for real-time decisioning and expanded its device intelligence capabilities, enhancing both the speed and breadth of fraud detection.

Setup Considerations:
Sift’s custom pricing may be less accessible for smaller businesses. Integration and continuous tuning are required for optimal results, and some advanced features are only available in higher-tier plans.

5. Signifyd

Platform Summary:
Signifyd is a fraud protection platform that offers automated decisioning, a global identity graph, and a unique chargeback guarantee, shifting financial liability away from merchants.

Core Features:

• Automated decisioning and global identity verification.
• Chargeback guarantee and liability shift for financial certainty.
• Real-time analytics and reporting for operational insight.
• Expanded global identity graph and analytics dashboard.

Primary Use Cases:

• Instantly evaluating and deciding on high-volume eCommerce transactions.
• Verifying new business customers and preventing reseller fraud in B2B retail.
• Removing financial risk from fraudulent chargebacks.

Recent Updates:
Signifyd has expanded its global identity graph and improved analytics dashboards, providing enterprise clients with more comprehensive fraud insights and operational data.

Setup Considerations:
Best suited for high-volume merchants, Signifyd’s cost structure may be prohibitive for smaller businesses. Liability coverage requires sharing transaction data, and customization of decision logic is less flexible than some competitors.

6. Kount

Platform Summary:
Kount delivers fraud scoring and omnichannel protection, supporting payment processors and B2B merchants with customizable policy management and device intelligence.

Core Features:

• Advanced fraud scoring and device fingerprinting.
• Flexible policy management for tailored fraud controls.
• Access to a global fraud network for emerging threat detection.
• Enhanced device intelligence and policy management UI.

Primary Use Cases:

• Screening CNP transactions for payment processors and B2B merchants.
• Risk and compliance screening for B2B merchant onboarding.
• Protecting against fraud across online, mobile, and in-store channels.

Recent Updates:
Kount has recently added enhanced device intelligence features and improved its policy management user interface, making it easier for enterprise users to configure and monitor fraud controls.

Setup Considerations:
Subscription pricing can be complex for businesses with fluctuating transaction volumes. Initial setup and rule configuration may require significant time and resources, and some features depend on integration with third-party platforms.

What is Card-Not-Present (CNP) Fraud Detection?

Card-not-present (CNP) fraud occurs when a criminal uses stolen credit card information to make a purchase online, over the phone, or via mail order, without the physical card being present. CNP fraud detection tools are sophisticated software solutions designed to combat this threat. They operate in real time, analyzing hundreds of data points for each transaction-such as device fingerprinting, geolocation, IP reputation, and behavioral biometrics. By leveraging advanced technologies, these platforms assess the risk level of each transaction, automatically blocking high-risk attempts and flagging suspicious ones for review, all while allowing legitimate customers to transact without friction. For a deeper look at the latest trends and prevention strategies, see this guide to CNP fraud.

Why is CNP Fraud Detection Crucial for B2B Companies?

For B2B companies, the stakes of CNP fraud are exceptionally high. Transactions are often of significantly greater value than in B2C commerce, meaning a single fraudulent order can result in substantial financial losses from chargebacks and lost goods. Beyond the direct costs, CNP fraud erodes trust with your business clients, damages your brand's reputation, and can lead to higher payment processing fees or even the loss of your merchant account. Implementing a robust fraud detection solution is not just a defensive measure; it's a strategic imperative to protect revenue, maintain partner confidence, and ensure scalable, secure growth in a digital-first economy. Explore the impact of credit card fraud on businesses for more insight.

How to Choose the Best CNP Fraud Detection Provider

Selecting the right provider requires a methodical approach focused on your specific business needs. Start by evaluating the core technology: does the provider use advanced, self-learning models that can adapt to new fraud tactics? Assess the ease of integration with your existing tech stack, including your e-commerce platform and payment gateway. Look for a solution that offers a balance between automation and control, allowing you to customize rules and risk thresholds. Finally, consider the provider's expertise and support. The best partners act as an extension of your team, offering transparent data analytics and strategic guidance to help you not only block fraud but also safely approve more legitimate transactions. For a comprehensive look at fraud detection and prevention solutions, review the available options tailored for enterprise needs.

Frequently Asked Questions

What is card-not-present (CNP) fraud and why is it a growing concern for B2B businesses?

Card-not-present (CNP) fraud occurs when transactions are made without the physical presence of a payment card, typically through online, phone, or mail order channels. For B2B businesses, the rise of digital payments and remote transactions has expanded the attack surface for fraudsters, making CNP fraud a significant and growing threat. Fraudsters exploit vulnerabilities in digital payment systems, leading to financial losses, reputational damage, and increased compliance burdens. As transaction volumes and values increase in B2B environments, robust fraud detection tools are essential to safeguard revenue and maintain trust.

How do advanced analytics enhance CNP fraud detection compared to traditional rule-based systems?

Modern analytics technologies analyze vast amounts of transaction data in real time, identifying subtle patterns and anomalies that traditional rule-based systems may miss. Unlike static rules, these models continuously learn from new fraud tactics, adapting to evolving threats without manual intervention. This reduces false positives, improves detection accuracy, and enables proactive identification of emerging fraud schemes. For B2B organizations, this means more effective fraud prevention with less operational friction and fewer disruptions to legitimate business activity. For more on the technology behind these solutions, see advanced analytics for fraud prevention.

What compliance considerations should B2B organizations keep in mind when selecting a CNP fraud detection tool?

B2B organizations must ensure that their chosen fraud detection tool supports relevant regulatory requirements, such as PCI DSS, GDPR, and industry-specific mandates. Key compliance features to look for include secure data handling, audit trails, explainable models for transparent decision-making, and integrations with compliance management systems. Some platforms also offer automated policy monitoring and entity monitoring to streamline compliance workflows. Selecting a solution with strong compliance capabilities helps reduce regulatory risk and simplifies audits for compliance officers and internal auditors.

How do these fraud detection platforms integrate with existing B2B payment and risk management systems?

Most leading CNP fraud detection platforms offer flexible integration options, including APIs, pre-built connectors for payment gateways, CRMs, and ERPs, and modular architectures that allow organizations to implement only the features they need. Integration considerations include compatibility with current payment processors, ease of data orchestration, and the ability to unify risk management across multiple business units or channels. Some platforms, like FraudNet, emphasize no-code configuration and seamless data ingestion, reducing the need for developer resources and minimizing operational disruption during deployment.

What are the key factors to consider when choosing the best CNP fraud detection tool for a medium-sized or enterprise B2B organization?

When selecting a CNP fraud detection tool, B2B organizations should evaluate several factors: the sophistication of analytics capabilities, compliance features, integration flexibility, scalability, industry focus, and total cost of ownership. It’s important to assess how well the tool aligns with your specific use cases (e.g., eCommerce, marketplaces, international payments), the level of automation and customization available, and the vendor’s track record in supporting similar businesses. Additionally, consider the quality of customer support, ongoing product updates, and the ability to adapt to future fraud and compliance challenges as your business grows. For more guidance, review the best tools for detecting payment fraud.

Disclaimer: This article is based exclusively on publicly available information. The tools referenced have not been independently tested by us. Should you identify any inaccuracies or wish to provide recommendations, we invite you to contact us.