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A PYMNTS Intelligence report found that 59% of surveyed companies using at least three AI tools stopped 90% or more of attempted payment-fraud attacks before losing money. The finding is based on a July survey of 150 U.S. finance and treasury executives; the report also says 57% of respondents had no AI fraud detection tools.
59% of companies using at least three AI tools to fight payments fraud said they stopped at least 90% of attempted attacks before suffering a loss, compared with 32% of firms without AI defenses, according to a PYMNTS Intelligence report published in September. The survey findings point to a potential advantage from combining detection technology with other payment controls, but they do not establish that AI alone caused the difference.
The report, Prevention First: Building a Smarter Defense Against Payments Fraud, was produced by PYMNTS Intelligence in collaboration with Bottomline. Its findings draw on a July survey of 150 treasury and finance executives at U.S. companies with annual revenue of at least $100 million. The research focused on how businesses protect payments to suppliers.
The 59% figure applies to companies using three or more AI tools and reporting that they prevented at least 90% of attempted fraud before taking a loss. The comparable share among companies without AI defenses was 32%, a 27-percentage-point gap. The survey reports an association between the use of multiple tools and prevention outcomes; the material provided does not describe a controlled test or establish causation.
Among companies using AI for fraud detection, 77% said it performed better than previous methods, while 8% said it performed worse. Adoption included real-time risk scoring, used by 83% of AI adopters; automated document verification, used by 82%; and scanning incoming messages for signs of AI-generated fraud, used by 78%. These are separate reported adoption figures, not measures of how much loss each tool prevented.
Layered Controls May Cut Payment Risk
The figures matter to finance teams because supplier-payment fraud can divert money before a company discovers that an account or payment request was fraudulent. The report’s findings suggest that businesses adopting several AI tools may be better positioned to identify suspicious activity early. They also point to the importance of pairing software with supplier verification, account validation and approval procedures.
The report describes examples in which monitoring helped flag an unexpected change before a wire transfer went to a fraudulent account, and a payment was stopped when an ACH transaction differed from a supplier’s usual activity. These examples illustrate possible safeguards, but the source does not provide enough detail to quantify their frequency or independently verify their outcomes. The broader operational challenge is to screen suspicious payments without creating unnecessary delays for legitimate ones.
Adoption remains a significant issue: 57% of surveyed companies had no AI fraud detection tools. Of that group, 58% said they were implementing the technology or expected to adopt it within 12 months. The findings may help companies weigh possible benefits against implementation needs, but they are not a guarantee that adding AI will prevent a particular business from suffering fraud.
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Survey Scope and Adoption Plans
The research is based on executives’ responses in one survey conducted in July 2026, rather than a year-by-year comparison or a broad census of businesses. All surveyed firms were in the United States and had annual revenue of at least $100 million. The reported percentages therefore describe this sample and should not automatically be applied to smaller companies or organizations in other countries.
The report also records planned investments among respondents: 81% planned to improve supplier onboarding verification, 75% expected to invest in bank account validation before payment, and 73% planned to enhance AI-driven transaction monitoring. These figures describe intentions, not completed spending or measured results. Together, they show that the report treats fraud prevention as a combination of tools and payment processes, not a software-only problem.
““Prevention First: Building a Smarter Defense Against Payments Fraud””
— PYMNTS Intelligence report, produced with Bottomline
What the Survey Cannot Establish
The published figures do not show whether the firms using multiple AI tools had other characteristics—such as larger security budgets, more mature payment controls or different fraud exposure—that contributed to their results. The source material does not provide the survey questionnaire, detailed sampling methods, margins of error or a breakdown by industry. That limits how precisely the figures can be generalized beyond the respondents.
It is also unclear what respondents counted as an attempted attack, how they measured the share prevented, or how long their prevention outcomes were tracked. The report says 77% of AI adopters rated detection as better than previous methods, but it does not provide a financial return-on-investment estimate or establish how much loss the tools prevented in dollars. The 58% adoption figure reflects stated implementation plans or expectations, not confirmed future deployments.
Planned Controls Will Test Adoption
The next measurable development will be whether companies that said they planned to adopt or strengthen controls actually do so, and whether later reporting documents changes in fraud-prevention outcomes. The survey says some non-adopters expected to implement AI within 12 months, while many companies planned improvements to onboarding checks, bank-account validation and transaction monitoring.
For now, the report supports a limited conclusion: among the surveyed large U.S. companies, firms using at least three AI tools more often reported preventing 90% or more of attempted fraud before a loss than firms without AI defenses. Further evidence would be needed to determine how much of that difference came from the technology itself, the controls used alongside it, or other differences between companies.
Key Questions
What does the 59% figure measure?
It is the share of surveyed firms using three or more AI tools that said they stopped at least 90% of attempted payments-fraud attacks before suffering a loss. It is a survey result, not a guarantee for other businesses.
How did firms without AI defenses compare?
32% of companies without AI defenses reported stopping at least 90% of attempted attacks before a loss. The report describes a 27-percentage-point difference from the 59% share among multi-tool AI adopters.
Which AI tools did companies report using?
Among AI adopters, 83% used real-time risk scoring, 82% used automated document verification, and 78% scanned incoming messages for signs of AI-generated fraud.
Does the report prove that AI prevents fraud?
No. The survey identifies an association between using multiple AI tools and reported prevention outcomes. The source material does not establish causation or show that AI alone produced the difference.
Who took part in the survey?
The July survey included 150 treasury and finance executives at U.S. companies with annual revenue of at least $100 million. The findings may not represent smaller firms or businesses outside the United States.
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