AI is helping banks save millions by transforming payment fraud prevention

• On average, organisations lost $60 million to payment fraud in the past year, according to research by Mastercard.  
• Synthetic identity fraud and impersonation scams are rising quickly, fuelled by generative AI (gen AI). 
• AI tools that use real-time data and behaviour insights can help stop fraud before it happens — and reduce losses. 
• 83% of industry leaders say AI has reduced false positives and churn, marking a new era in fraud prevention.

The estimated global financial impact of fraud grew to more than $485 billion in 2023[1] — a staggering number that is expected to grow in years ahead. 

One catalyst? Fraudsters are using gen AI to quickly produce convincing deepfakes, synthetic voices and forged documents to run social engineering scams at scale. According to a report from Deloitte, gen AI could fuel $40 billion in U.S. fraud losses by 2027 — over triple the $12.3 billion seen in 2023.

But banks are stepping up, too — using AI to fight back faster. In the past, fraud prevention teams set manual rules to decide which transactions to approve or block. Now, AI can analyse data to detect unusual patterns and make smarter authorisation decisions in real-time. 

This shift is already paying off. Mastercard’s 2025 payment fraud prevention report, produced in partnership with Financial Times Longitude, found that 42% of issuers and 26% of acquirers ​​​​​​​​have saved more than $5 million in fraud attempts over the past two years thanks to AI. But to make the most of AI for fraud prevention, organisations need high-quality data that feeds into models for better risk decisions.

Mastercard’s research found that organisations lost an average of $60 million to payment fraud in the past year. And as AI helps fraudsters create more convincing scams, those losses are expected to continue rising:

• Criminals harness AI to analyse large volumes of publicly available data and craft highly targeted behavioural scams. 
• With affordable gen AI tools, fraudsters can create convincing phishing messages, synthetic voice clones and deepfake videos at scale, making it easier to manipulate victims into approving fraudulent transactions.

At the same time, as real-time payments gain popularity, financial institutions have only a short window to identify and block AI-powered fraud attempts. That’s why payment industry leaders are keeping a close eye on a variety of fraud risks:

• Leaders see synthetic identity fraud (61%), impersonation scams (60%) and cross-border fraud (54%) as the fastest-growing threats over the next year. Synthetic identity fraud, which involves combining stolen personal information with fabricated details to create a fake identity, is escalating as fraudsters use AI to sift through massive datasets and build more convincing profiles.
• Other growing risks include e-commerce fraud (47%), Buy Now, Pay Later (BNPL) abuse (42%) and deepfakes (21%). Concerns about e-commerce fraud and BNPL abuse, when bad actors make unauthorised BNPL accounts or transactions, underscore the need for stronger customer protections across the digital payments landscape.

Ninety per cent of payment leaders expect higher financial losses in the next three years if they don’t increase their use of AI in fraud prevention. 

Fortunately, there are various ways to apply AI for fraud prevention, from analysing transaction patterns to reducing manual reviews. Many institutions are already generating high return on investment (ROI) as a result:

• 85% of respondents report seeing returns from using AI for fraud case triage and investigation, transaction pattern recognition and real-time detection of suspicious transactions.
• 83% say AI has significantly speeded up their process for fraud investigation and case resolution.

​​​Still, it’s important to remember that sustained investment leads to the biggest gains. Organisations that have used AI for over five years report saving $4.3 million in lost revenue, almost double the average savings of $2.2 million. 

Simultaneously, leaders know that keeping AI current as fraud tactics evolve is a significant hurdle. To defend against new and emerging threats, AI tools need to learn and adapt in real time. 

With AI tools that incorporate real-time data and behaviour insights, organisations can make more efficient authorisation decisions to increase approval rates and keep customers happy.

The problem: Historically, banks have used rules-based systems to approve or block transactions. For example, a bank may set a rule to flag purchases over a certain dollar amount or deny transactions originating from unusual locations. But here’s the issue: Rigid, manual processes can slow fraud detection, especially as scams get faster and more complex.

The solution: AI solutions can easily overcome that limitation, analysing millions of data points to quickly evaluate transaction risk and offer real-time insights. This means banks can spot emerging threats as they unfold and quickly make informed decisions, minimising detection lag.

It’s no surprise that 80% of organisations reported that AI helped eliminate unnecessary manual reviews. As AI enables issuers and acquirers to anticipate threats earlier, it also benefits fraud teams by freeing up capacity for more complex investigations.

The problem: Because static authorisation rules lack nuance, they often result in false positives, i.e., when legitimate transactions are incorrectly identified as fraudulent and declined or flagged for review. Beyond creating extra work for internal teams, this also hurts the customer experience. 

The solution: Advanced AI models can analyse diverse data points to assess fraud risk with precision. For example, if a customer who regularly buys mid-range clothing suddenly purchases several luxury fashion items during a seasonal sale, advanced AI models can analyse factors like historical purchase behaviour, Retailer credibility and timing to determine if this spike in spending is legitimate behaviour.

This context-aware intelligence drives more accurate authorisation decisions that prevent fraud without increasing friction for customers. In fact, 83% of respondents report that AI has significantly reduced false positives and customer churn rates in the past year.

The problem: While AI can support real-time fraud detection, it requires high-quality data to remain effective. Leaders understand this demand, with 64% of respondents saying they need to accelerate access to new, credible data sources to keep pace with evolving threats.

The solution: Effective AI fraud detection models integrate inputs from across the payment ecosystem, including card network intelligence, Retailer data and insights from consumers’ digital identity. 

For example, an AI tool might assess a Retailer’s risk based on historical fraud rates while also analysing a customer’s velocity count, which tracks how frequently actions like purchases or account changes occur within a short period.

Going forward, organisations’ success with AI fraud detection will depend on two factors: a model’s capacity to analyse large volumes of data and its ability to combine historical patterns and fresh information to assist with decision-making.

Generative AI is changing the fraud landscape. Fraudsters are faster and more adaptive with AI — and financial institutions must be, too.

Mastercard’s Decision Intelligence solution uses AI and network insights to analyse and score transactions based on risk level. With rich, real-time insights, you can make confident authorisation decisions, approve more genuine transactions and protect revenue as fraud tactics continue to evolve. 

Ready to transform your fraud defences with AI? Find out how Mastercard can help or read our report for all the survey insights.