A guide to enterprise fraud management

• Managing risk effectively
• What is enterprise fraud management?
• How does enterprise fraud management work?
• How can you implement enterprise fraud management?
• What does Visa offer?
• FAQs

The growth of digital commerce has made connecting with customers online easy, but it’s also introduced increasingly interconnected and sophisticated security risks. In a recent global survey, merchants say they are struggling to stay ahead of threats due to operational inefficiencies, resource constraints and the challenge of navigating fraud across complex channels and markets.¹

Having a robust enterprise fraud management strategy in place is essential, as vulnerabilities in global businesses can be exploited, leading to decreased acceptance rates, revenue losses and poor customer retention. Visa’s Value-Added Services can support your ability to manage risk effectively and confidently accept more legitimate orders.

Enterprise fraud management, sometimes referred to as digital fraud management, is a strategy that’s designed to detect, prevent and respond to fraud risks across a business’s operations. Unlike solutions that address just one channel or one type of fraud, enterprise fraud management takes a holistic approach. It does this by bringing together risk-management expertise (traditionally associated with fraud teams) and technology expertise (traditionally associated with cybersecurity teams) to create a unified program.

Enterprise fraud management goals include:

• Reducing payment fraud — unauthorized transactions, stolen cards or chargebacks
• Reducing non-financial fraud — policy abuse, scams, promotion misuse
• Reducing the associated costs of fraud investigation and remediation
• Reducing friction for genuine customers and avoiding ‘customer insults,’ where legitimate customers are wrongly blocked. In turn, this can improve the customer experience

Key operational components of effective enterprise fraud management include:

• Identity verification: Confirming the true identity of a customer or account holder
• Risk scoring: Assigning risk scores to transactions, accounts or users based on behavioral, historical, device, geographic and other signals
• Customer authentication: Applying appropriate authentication steps (for example, multi-factor authentication) when risk is elevated
• Case management and reporting: Tracking suspicious events, investigating cases and reporting outcomes for continual improvement

But in implementing a modern enterprise fraud management strategy, many merchants face serious technological and data-related hurdles. Over 80% struggle with effectively using their data and improving the accuracy of artificial intelligence (AI) and machine learning-powered tools, and around two-thirds (67%) say one or more of these data and technology challenges negatively impact their ability to manage fraud.¹

Enterprise merchants are significantly more likely than smaller businesses to use tokenization and offer a wider range of payment methods. As a result, many are leading the way in adopting stronger security measures. And their efforts are being backed up by a shift in the way they allocate their resources. Instead of adding more staff, many are investing in advanced tools and technologies that automate fraud detection and fraud prevention. Nearly two-thirds (63%) are boosting their investment in fraud management technology. Merchants now screen more than twice as many orders digitally (52%) as they do manually (23%), relying on this automated approach to manage large transaction volumes efficiently and accurately.¹

The overarching goal of enterprise fraud management is to prevent, detect and intercept various forms of fraud while reducing investigation costs and, crucially, improving the customer experiences. Enterprise fraud management is an integrated, multi-stage process that’s driven by advanced technology, as outlined here:

1. Core components: Enterprise fraud management systems are built upon several key components that are utilized throughout the customer lifecycle, including identity verification, risk scoring, customer authentication, case management and reporting.

2. Automated risk decisioning: At the heart of enterprise fraud management is automated risk decisioning powered by AI and machine learning (ML). Working in real time, these systems screen transactions within milliseconds to deliver precise risk assessments. AI models analyze hundreds of data points — such as identity, location, device and behavioral patterns — to assign a risk score from 0 (low risk) to 99 (high risk). This score integrates with a configurable rules engine, allowing merchants to align fraud policies with business goals.

3. Proactive and multi-stage security: Enterprise fraud management safeguards every stage of the customer journey through layered, proactive defenses. Merchants use intelligent monitoring tools to detect risk signals from account creation to refund or dispute — over half now track activity at checkout, payment and post-transaction stages, with refund-stage monitoring rising from 45% to 57% year over year. Advanced authentication, such as Visa Secure, helps verify customer identity and offers fraud liability protection. Visa’s Token Management Service (TMS) further secures sensitive payment data by replacing card numbers with unique tokens. Enterprise fraud management also defends against account takeovers and helps merchants effectively dispute fraudulent chargebacks.

Who is affected by enterprise fraud and how?

Enterprise-level merchants — those operating across multiple markets, channels and payment types — face a uniquely complex and aggressive fraud environment. Compared to small and medium-sized businesses (SMBs), they encounter:

• Higher transaction volumes that create a broader ‘attack surface’ for fraudsters to potentially exploit
• More diverse channels, including websites, mobile apps, call centers and physical stores
• A wider variety of payment methods, such as digital wallets, Buy Now, Pay Later (BNPL) and local options, which expand opportunities for growth, but also for fraud.

When enterprise fraud management systems are weak or disconnected, the consequences multiply: higher financial losses, greater operational costs and lost revenue from legitimate customers who abandon the checkout process after false declines — sometimes called ‘customer insults.’

Why does enterprise fraud management matter?

A strong enterprise fraud management system helps merchants:

• Accept more legitimate orders by lowering false declines
• Reduce fraud losses and chargebacks
• Improve customer experience with smoother, faster checkouts
• Protect brand reputation and increase customer loyalty

With studies showing that fraud can also hurt conversion and retention, a successful enterprise fraud management strategy lies in balancing prevention with acceptance optimization.

Enterprise fraud management is about striking the right balance — preventing fraud without compromising customer experience or approval rates. False declines, where valid transactions are mistakenly blocked, can be more damaging to revenue and reputation than fraud itself. To succeed, merchants must combine strategy, technology and process to build a scalable, intelligent fraud management framework.

Strategic priorities behind enterprise fraud management

Merchants should focus on core priorities — reducing fraud and chargebacks, controlling operational costs and optimizing acceptance rates — while building the capabilities needed to respond quickly to emerging risks. These priorities lay the foundation for a more resilient and customer-centric fraud management approach. Below are the key priorities and best practices for enterprise merchants.

1. Reduce fraud and chargebacks while minimizing operational cost: The dual objective of effective enterprise fraud management is simple: protect revenue by preventing fraud and boost revenue by accepting more good transactions. Achieving both requires the right balance between automation and human oversight — reducing the cost of manual reviews, investigations and fraud team operations while improving decision accuracy.
2. Build a strong risk management foundation: Fraud management shouldn’t operate in isolation. It should be closely aligned with broader enterprise risk programs, including anti-money laundering (AML) and anti-terrorist financing (ATF) efforts. Embedding fraud risk into the wider business strategy ensures it’s treated as a strategic capability, not just an operational function.
3. Prioritize data and automation: As fraud becomes more complex — with criminals using tools like generative AI, bots and synthetic identities — manual reviews alone are no longer enough. The sheer volume and speed of transactions make relying solely on manual review both resource-intensive and unsustainable. To stay ahead, merchants need a smarter, more scalable process. By leveraging ML and AI, businesses can detect patterns at scale, identify threats earlier, automate decisioning and continuously learn from outcomes. In turn, freeing teams to focus on strategy and exception handling rather than routine checks.
4. Automate risk decisioning: Automation is at the operational core of modern fraud management. Combining ML risk scoring with a configurable rules engine enables real-time decisions — approve, decline or challenge — based on a transaction’s risk level. This reduces friction for legitimate customers, improves accuracy and ensures consistency across markets and channels.
5. Monitor outcomes and iterate: Merchants should regularly monitor performance indicators such as fraud losses, false declines, customer complaints and operational costs. Feeding these insights back into AI models and decisioning platforms ensures the strategy evolves as fraud patterns change.

How to implement an enterprise fraud management strategy

• Establish governance and cross-functional ownership: Fraud management should not just sit with the fraud team; it also needs input from payments, operations, customer experience, legal/compliance and data/analytics.
• Map your channels and payment methods: Understand all your sales channels (online, mobile app, physical store) and all payment methods (cards, wallets, BNPL, local methods) as this will give you full visibility of where fraud can enter.
• Audit your current fraud posture: What systems are in place? What percentage of orders are manually reviewed? What is your false decline rate? Are you using identity verification, device analytics, rules engines, ML?
• Improve data quality and integration: One of the biggest obstacles to effective enterprise fraud management is fragmented data and poor data quality. To overcome this, gather data from as many sources as possible — like payments, returns and chargebacks — and feed it into your decisioning platform. Equally important is data collaboration across the payments ecosystem. Sharing relevant signals with issuers, acquirers and network partners helps strengthen transaction decisioning on both sides. This ensures there’s enough information to approve legitimate transactions while accurately identifying and declining fraudulent ones.
• Select and deploy technology: Choose a decisioning platform that offers risk scoring and case management and integrates with your payment stack. Ensure it supports multiple channels and payment methods, and allows you to refine rules and models.
• Shift towards automation: The volume and complexity of today’s transactions make relying solely on manual review resource-intensive, costly and unsustainable. While manual review will still exist, the objective should be automation for the majority of payments and orders.
• Measure and optimize outcomes: Track key metrics like acceptance, decline and false decline rates, as well as the ratio of manual to automated reviews, time to decision, fraud loss as a share of revenue, chargeback rate and customer abandonment. Use those metrics to continuously refine your system.

Visa Protect for Acceptance delivers AI-driven, enterprise-grade fraud management that’s designed to help detect risk, reduce fraud and accept more legitimate transactions. It does this through tools like Decision Manager (DM), which is a scalable fraud management platform that automates risk decisioning in real time. Powered by Visa’s global network and ML models trained on insights from billions of annual transactions, DM analyzes hundreds of data points within milliseconds to generate precise risk scores. This enables merchants to reduce fraud and false declines, improve acceptance rates and lower operational costs by minimizing manual review.

In addition, Visa’s enhanced authentication solutions add an extra layer of identity verification before authorization — helping to stop fraudulent transactions before they happen. Visa also supports proactive fraud mitigation through:

• Account Takeover Protection (ATOP): Detects and prevents fraud during account creation and onboarding
• Token Management Service (TMS): Replaces sensitive card data with secure digital tokens for network-agnostic protection
• Verification Suite: Verifies account, customer and transaction details to reduce fraud and enhance efficiency

Through its ‘network-of-networks’ risk intelligence, Visa provides API-based risk signals using global data insights from across its network. Finally, Visa Consulting and Analytics (VCA) and Visa Managed Services (VMS) for Decision Manager offer tailored advice and support.