AI solutions for fraud prevention and detection

• Smarter risk management
• What is AI fraud detection?
• How does AI fraud detection work?
• How can businesses harness AI fraud detection?
• What AI fraud detection solutions does Visa offer?
• Results in action
• FAQs

Cybercriminals now use artificial intelligence (AI) to power faster, more targeted and more complex fraud attacks — from deploying adaptive malware and automating large-scale phishing campaigns to generating convincing deepfakes that can impersonate customers, employees or executives.

Yet the same technology fueling these ever-evolving threats also offers the most powerful defense, and AI is an essential ally in the fight against digital fraud. Unlike traditional rule-based systems or signature-dependent antivirus software that rely on known threat patterns that can quickly become obsolete, AI-powered models continuously learn from new data, detecting behavioral anomalies that indicate fraudulent activity before it escalates.

As a result, AI fraud detection systems can help distinguish between legitimate and suspicious transactions with greater accuracy, reducing the risk of false declines that frustrate customers and cost sales.

This is where we can help. Visa Acceptance Solutions delivers automated decisioning and smarter risk management for merchants, while helping banks combat fraud across the entire transaction lifecycle — from tokenization and authentication to authorization. Here, we look at how AI and machine learning (ML) are reshaping the fraud detection landscape.

AI fraud detection refers to the use of AI and ML to help identify and stop unauthorized or suspicious activity across the payment ecosystem in real time. Rather than relying solely on static rules or historical blacklists, AI systems continuously learn from patterns, data and behavior to continually adapt and detect emerging and ever-evolving threats.

AI fraud detection relies on models that analyze vast amounts of transaction data to determine the probability that a given payment is fraudulent. For each transaction, a risk score is generated, typically ranging from 0 (low risk) to 99 (high risk). This score helps merchants and payment providers make fast, accurate decisions about whether to approve, challenge or decline a transaction.

How AI and ML enhance fraud detection

Traditional fraud detection methods rely on manual reviews and static rules — for example, flagging transactions above a certain amount from an unfamiliar IP address. However, as the volume of digital transactions grows, this approach has struggled to scale. By contrast, today’s AI and ML fraud detection models continuously analyze large datasets — including transaction patterns, device fingerprints and behavioral biometrics — to detect anomalies that might be missed by manual reviews.

These models learn normal customer behavior and flag deviations. For instance, a returning customer suddenly making multiple high-value purchases from a new country at 3am could signal account takeover. The system can automatically block, verify or escalate the transaction — often in milliseconds. For AI and ML fraud detection models to be effective, they depend on three key elements:

1. Data volume: Effective ML requires immense datasets to learn from. Visa, for example, processes over 269 billion transactions each year, exposing its AI models to global spending behavior and fraud patterns.
2. Rich data: A diverse range of data points provides more ‘clues’ that can help identify fraud and distinguish legitimate customers. The richer the dataset, the better the model can generalize and detect anomalies.
3. Relevant data: AI systems also need accurate information, such as confirmed chargebacks, to train effectively. This feedback loop ensures the model can refine its predictions over time and recognize evolving fraud tactics.

As digital transactions continue to grow, merchants and financial institutions are focused on improving AI and ML accuracy and increasing automation across fraud detection and fraud prevention workflows — enabling faster, smarter and more efficient protection.

How is generative AI used in digital fraud?

Cybercriminals are increasingly using generative AI (GenAI) to create synthetic identities, deepfake videos and voices and forged digital documents that can bypass traditional verification methods. These hyper-realistic forgeries make it harder for both merchants and financial institutions to distinguish genuine customers from fraudulent actors. According to recent research, 56% of merchants are now using GenAI-powered fraud detection tools, with adoption expected to grow significantly.¹

The central aim of AI fraud detection is to increase accuracy. It achieves this by focusing on positive indicators and identifying good customers to accept more legitimate orders and cut down on false positive (or ‘customer insult’) rates. AI fraud detection operates by ensuring fast, real-time decisions. Transactions are screened within milliseconds using real-time AI risk scores. Below, we outline how AI fraud detection works in more detail:

1. Real-time risk scoring: When a transaction occurs, the AI model instantly evaluates hundreds of data points to calculate a risk score. This allows merchants to make immediate, informed decisions without disrupting the customer experience. The process involves analyzing key indicators such as:

• Customer identity: Has this user been seen before? Do they have a consistent transaction history?
• Velocity: Is the customer making multiple purchases unusually fast or across different geographies?
• Geolocation: Does the location of the device or IP address match typical behavior patterns?
• Device intelligence: Is the device recognizable, or does it show signs of tampering, emulation or bot activity?

By assessing these dimensions together, AI can help identify subtle anomalies that human reviewers or traditional systems might miss.

2. Reducing false positives: For merchants, one of the costliest challenges in fraud prevention is the false positive — legitimate transactions that are mistakenly declined. False declines not only lead to lost revenue but can also damage brand reputation and customer trust. AI helps minimize this issue by focusing on identification. In other words, looking for attributes that confirm legitimacy rather than simply screening for risk. This proactive approach allows merchants to accept more valid transactions while maintaining robust security controls.

3. Continuous learning and adaptation: Fraud patterns change constantly. Self-adapting ML models ensure that fraud detection systems evolve with them. These models automatically learn from new data, adjusting to detect emerging threats without requiring constant manual tuning.

4. Consortium-based learning: Unified consortium models, such as those used by Visa, leverage shared intelligence across a vast network of merchants and financial institutions. This collective insight strengthens fraud detection by recognizing patterns that individual organizations might not detect on their own. It also helps models understand what ‘good’ customer behavior looks like, enabling faster, more accurate decisions.

Across the payments ecosystem, both merchants and financial institutions face a shared challenge: prevent fraud without compromising trust or the customer experience. AI helps enable merchants, issuers and acquirers to make smarter, faster and more confident risk decisions at scale. Below are four key ways AI-driven fraud detection benefits both merchants and financial institutions.

1. Implementing AI-powered threat detection: AI-driven systems continuously monitor transactions, customer behavior and device data to detect anomalies in real time. For merchants, behavioral analytics help spot early signs of account takeover or automated bot attacks, reducing chargebacks and inventory losses. For banks and issuers, AI threat detection enhances decision-making at the authorization stage, helping to reduce false declines that frustrate legitimate customers.

2. Prioritizing continuous improvement: AI and ML models improve with every transaction they process. By continuously training on vast datasets, these systems become smarter and more accurate over time. For financial institutions, investing in adaptive AI models helps enable faster identification of new fraud typologies, even before they are widely reported across the network. For merchants, AI-powered automation handles large transaction volumes efficiently, allowing fraud teams to focus on complex investigations and strategy rather than manual reviews.

3. Combining ML with custom rules engines: The predictive power of AI works best when paired with business-specific rules. Combining ML with a customizable rules engine — like Decision Manager (DM) — helps fine-tune fraud decisions:

• Merchants can tailor risk thresholds by market, product or customer type to maintain high approval rates
• Banks and acquirers can align fraud strategies with regional compliance and regulatory expectations while optimizing authorization performance

This balances proactive fraud control with conversion and approval optimization, turning fraud management into a growth enabler rather than a barrier.

4. Strengthening protection against adaptive malware: Cybercriminals use adaptive malware and zero-day exploits to infiltrate merchants’ systems or compromise financial institutions’ networks. AI-enhanced endpoint detection and response systems can help detect these attacks in real time. By learning normal system behavior, AI can identify subtle deviations — such as unauthorized data access or abnormal API calls — and automatically isolate affected devices or accounts. For merchants, this reduces downtime and prevents large-scale data breaches. For banks and payment providers, it strengthens the integrity of customer data, protecting the broader financial ecosystem from downstream risk.

Visa leverages its vast network data and sophisticated AI platforms (in accordance with applicable laws, regulations and contractual obligations) to deliver a powerful suite of fraud detection and risk scoring solutions, helping merchants and financial institutions achieve automation and higher accuracy.

• Decision Manager (DM): Built on Visa’s infrastructure, Decision Manager is a core ML fraud management platform that automates risk assessment at scale. It delivers the advanced fraud screen risk score, which rates each transaction from 0 (low risk) to 99 (high risk). The score draws on hundreds of real-time data points and insights from billions of transactions across VisaNet, helping merchants and acquirers instantly gauge risk.
• Visa Advanced Authorization (VAA) and Visa Deep Authorization (VDA): These models use AI to analyze every transaction. VAA identifies emerging fraud patterns and unusual usage behaviors in real time, helping issuers stop suspicious activity before authorization. VDA, which is purpose-built for card-not-present environments, uses deep learning to model long-term cardholder and merchant behavior, enhancing risk scoring for eCommerce transactions.
• Visa Consumer Authentication Service (VCAS): For eCommerce authentication, VCAS provides an AI-driven 3D Secure (3DS) solution that helps issuers authenticate in real time. By analyzing behavioral and contextual data, VCAS helps authenticate legitimate customers with minimal friction during a transaction.
• Visa Account Attack Intelligence (VAAI) and Enumeration Defense: Enumeration attacks — where fraudsters test stolen credentials through repeated low-value attempts — are rapidly increasing. VAAI helps detect and scores these attempts using AI-driven pattern analysis, enabling issuers to block fraudulent activity in real time.
• Continuous innovation and expertise: Visa’s AI capabilities are supported by dedicated teams of PhD data scientists and fraud specialists with decades of experience. Through ongoing collaboration with Visa Advanced Analytics, these teams continuously refine and patent new AI models that evolve with the global payments landscape — empowering merchants and financial institutions to stay ahead of next-generation fraud.

Decision Manager reduces manual reviews by 25%+²

Decision Manager (DM) — a core component of Visa’s AI-powered security ecosystem — enables merchants to balance protection with growth by automating risk decisions at scale while reducing friction for genuine customers. For active users, Decision Manager has helped reduce manual reviews by 25% or more, freeing fraud teams to focus on complex or high-value cases rather than routine screening. Its Identity Behavior Analysis capabilities further enhance performance by analyzing behavioral patterns to identify legitimate customers with greater confidence. This allows merchants to accept more good transactions, in turn improving sales conversion and customer satisfaction. In 2023, Decision Manager screened 3.2 billion transactions and prevented an estimated $33 billion in potential fraud losses — with 98.7% of all transactions processed through Decision Manager resolved automatically by AI.

The wider Visa ecosystem continues to earn industry recognition for its leadership in AI and ML. Visa Protect, which includes Decision Manager, Visa Advanced Authorization and other fraud prevention services, has been named a Leader in Enterprise Fraud Solutions by IDC.