Smarter transaction monitoring for secure payments

• Robust payment monitoring
• What is transaction monitoring?
• How does transaction monitoring work?
• What transaction monitoring solutions does Visa offer?
• Use cases
• FAQs

Effective fraud management can feel like a balancing act — between minimizing losses and maximizing legitimate transactions. Put simply, it’s about maintaining a secure environment that doesn’t compromise the customer experience. Transaction monitoring is at the center of achieving that balance.

For businesses and financial institutions, the challenge is to deliver fast, frictionless payments while keeping fraud and regulatory risks under control. Transaction monitoring makes this possible. It detects suspicious activity, supports compliance and protects both revenue and reputation — all without slowing down the customer experience.

As real-time payments and peer-to-peer transfers grow, so does the risk associated with irrevocable transactions — around 45% of merchants now face this issue. Robust monitoring helps keep pace while striking a balance between enhanced security and customer experience.

Transaction monitoring is the continuous process of reviewing and analyzing payment activity to detect unusual behaviors, patterns or thresholds that signal potential payment fraud or other financial risks. It analyzes transactions to flag and stop any activity that doesn’t look right before it has the chance to cause harm and seeks to identify broader patterns of suspicious behavior that may indicate money laundering, terrorist financing or other financial crimes. For merchants and financial institutions, transaction monitoring provides a real-time view of risk. It helps them validate legitimate activity, stop suspicious payments and ensures ongoing compliance with anti-money laundering (AML) and anti-terrorist financing (ATF) regulations.

It does all this in real time, which is important because, unlike traditional payments that allow time for manual review, real-time payments (RTP) settle instantly and are often irrevocable. This means fraud or laundering attempts can move between funds before intervention is possible.

Monitoring spans the full payment journey — from checkout to settlement, refunds and disputes — and many businesses are extending this oversight to the pre-purchase (such as browsing or cart evaluation) and post-fulfilment to ensure a more comprehensive approach to fraud detection.

What are Account Funding Transactions (AFTs)?

Account Funding Transactions (AFTs) are payments that ‘pull’ funds from a card to fund a non-merchant account. They work much like a purchase transaction, but instead of paying a merchant, the money moves into another account or wallet. AFTs are common in everyday financial activity. Examples include:

• Loading or topping up prepaid card
• Transferring money into savings or investment accounts
• Funding person-to-person (P2P) transfers
• Adding money to third-party digital wallets

Because AFTs often move funds in real time, they carry a higher fraud risk. Fraudsters may try to exploit these transactions to gain unauthorized access to cardholders’ funds. To prevent this, issuers and processors use real-time decisioning systems that assess each transaction within milliseconds. These systems apply risk scores, verify authenticity and block suspicious activity automatically. Continuous, artificial intelligence-driven monitoring of AFTs helps detect and stop threats before they reach the customer, keeping payments flowing quickly and securely.

Transaction monitoring analyzes payment activity in real time to spot unusual patterns or behaviors that could signal fraud, money laundering or compliance risks. It’s a process that relies on a combination of threshold rules, behavioral analytics and advanced tools such as artificial intelligence (AI), machine learning (ML) and predictive scoring. Each transaction is compared against normal activity and past data. When something looks irregular — like an unusual number of transactions, unexpected locations or inconsistent customer behavior — the system can flag or block it, trigger extra checks or alert compliance teams.

How does transaction monitoring detect unusual behavior?

Transaction monitoring platforms combine multiple analytical layers to identify potential threats:

• Threshold and velocity rules: Flag rapid or unusually large payments, high-frequency activity or refunds to different accounts than the original purchase.
• Behavioral analytics: Compares a customer’s or merchant’s normal activity — such as location, timing, spend — to detect deviations, like sudden high-value payments from new locations or devices.
• Entity-level risk scoring: Evaluates attributes such as email addresses, phone numbers, device IDs, IP addresses and other identifiers for hidden connections or anomalies. The resulting risk score is used to inform an overall transaction risk assessment.
• AI, ML and predictive models: Continuously refine their understanding of what ‘suspicious’ looks like by analyzing payment data. For example, Decision Manager (DM) assesses billions of global transactions to generate a predictive risk score from 0 (low risk) to 99 (high risk).
• Post-transaction monitoring: Tracks disputes, chargebacks and refunds to identify fraudulent returns or money-laundering schemes.

As a result, transaction monitoring takes a layered approach that allows for the detection of both individual anomalies and broader network-wide fraud patterns.

Who uses transaction monitoring?

Transaction monitoring affects a wide range of stakeholders across the payment ecosystem:

• Banks and issuers use it to detect and stop unauthorized or suspicious account activity before losses occur.
• Payment processors, gateways and acquirers rely on it to screen transactions across merchants, channels and geographies, ensuring safe and compliant payment flows.
• Merchants use transaction monitoring tools to safeguard revenue, prevent chargebacks and build customer trust.
• Regulators and compliance officers depend on monitoring outputs to ensure anti-money laundering (AML) and anti-terrorist financing (ATF) obligations are met.

Because these systems sit at the heart of digital commerce, effective transaction monitoring directly impacts security, compliance and customer experience.

Why is transaction monitoring important for compliance?

Transaction monitoring is an important part of fraud detection and ensuring overall financial security. As well as enabling businesses to detect and stop fraud, it helps them comply with regulations and maintain customer confidence. Without sufficient safeguards in place, businesses risk significant financial loss, reputational damage and regulatory penalties.

Every business that handles payments must comply with strict AML and ATF rules, which require continuous oversight of customer transactions to prevent illicit finance. Transaction monitoring systems make this possible by giving higher scrutiny to high-risk customers, products or regions. In practice, these systems assess transactions against pre-defined thresholds — such as transaction amount or frequency — and generate alerts for anything outside of expected patterns.

How does AI improve transaction monitoring?

Traditional rule-based transaction monitoring systems can detect known payment fraud patterns, but they struggle to identify new or evolving threats. Today’s advanced AI and ML models extend these capabilities by predicting the probability of fraud before it happens. Unlike static rule sets, AI-powered systems continuously learn from both historical data and real-time transaction feedback, allowing them to adapt and improve over time.

Leading solutions in this space include Decision Manager (DM) — a scalable fraud management and automated risk decisioning platform — and Featurespace’s ARIC Risk Hub, a next-generation AI model for real-time fraud detection. In addition, behavioral and identity-based tools such as Risk-Based Authentication (RBA) add another layer of protection by verifying users based on their behavior and context.

AI-powered transaction monitoring can:

• Identify subtle correlations between entities across millions of data points.
• Detect new and evolving instances of fraud without the need for explicit rules.
• Continuously recalibrate to balance fraud detection with transaction approval rates.

This predictive approach allows financial institutions and merchants to be proactive rather than reactive, catching suspicious behavior before losses or regulatory breaches occur.

What’s the difference between fraud detection and transaction monitoring?

Fraud detection and transaction monitoring are closely connected, but they serve different purposes within risk management.

Fraud detection focuses on identifying and blocking specific fraudulent transactions in real time. It relies on data, behavioral analytics, risk scoring, AI and ML to recognize patterns that suggest fraud — such as stolen credentials, unusual spending or account takeovers. For merchants, this helps prevent chargebacks and protect revenue. For banks, it stops unauthorized access before funds are lost.

Transaction monitoring, on the other hand, provides continuous oversight of all payment activity. So while fraud detection focuses on preventing and stopping immediate threats, transaction monitoring focuses on detecting ongoing or systemic risks.

Visa provides a suite of connected risk solutions designed to help strengthen transaction monitoring and fraud detection.

Decision Manager (DM) seamlessly integrates across Visa’s transaction monitoring ecosystem, empowering merchants with a unified, AI-driven fraud prevention framework. It leverages Visa’s global data network and advanced analytics to assess risk in real time, helping merchants reduce chargebacks, improve approval rates and maintain compliance.

Risk scoring

• Visa Advanced Authorization (VAA): Delivers a risk predictive score for authorization transactions by analyzing global network data and identifying patterns that deviate from normal cardholder behavior.
• Visa Deep Authorization (VDA): Uses deep learning to assess card-not-present (CNP) transactions. It builds a behavioral history for each cardholder and merchant to identify high-risk activity.

Decisioning

• Visa Risk Manager (VRM): This real-time decision engine uses VisaNet data to apply customized, rule-based controls. VRM provides issuers with control and flexibility to manage risk strategies and tolerance across all card payments. Using the VAA Risk score along with 70+ transaction parameters,¹ VRM helps issuers build targeted rules to detect high-risk transactions while maintaining legitimate approvals.

Alerts and controls

• Visa Transaction Controls (VTC): Sends real-time, customizable alerts when a transaction is declined and lets cardholders manage where and how their cards are used — including the ability to turn cards on or off instantly.
• Visa Purchase Alerts: Provides near real-time transaction notifications, while the premium version adds enhanced control options for cardholders.
• Tokenization: Protects sensitive payment data across all channels — card-present, eCommerce, mobile and IoT — ensuring customer information remains secure.

Analytics and insights

• Visa Analytics Platform (VAP): Offers data-driven insights and benchmarks to help issuers reduce fraud and improve authorization rates. Detailed reports track fraud and liability trends by channel, region, merchant and fraud type, enabling faster strategic adjustments.

Oversee the entire transaction lifecycle

Visa’s transaction monitoring solutions give financial institutions and merchants the tools to oversee the entire transaction lifecycle, helping detect and prevent fraud before it impacts customers. Through Visa Consulting and Analytics (VCA), clients gain expert guidance drawn from Visa’s global experience working with leading financial institutions. While for additional support, organizations can choose from flexible managed services models that enhance internal fraud and risk teams or engage Visa Advisory Services for tailored support, such as rule optimization reviews and strategic program enhancements.