AI has changed the rules of deception. What once took a skilled fraudster hours or days now takes a model seconds. Just in time for International Fraud Awareness Week 2025, 16-22 November 2025, a new cross-industry survey of anti-fraud professionals by the Association of Certified Fraud Examiners (ACFE) and data and AI leader SAS finds that:

    77% report an acceleration in deepfake social engineering over the past 24 months; and
    83% anticipate a moderate (28%) to significant (55%) increase in such schemes in the next two years.

These early insights – previewing the fourth edition of the Anti-Fraud Technology Benchmarking Report, coming in March 2026 – signal a rapid rise in AI-fuelled fraud that is escalating risks for both industry and the public.

“Artificial intelligence has become one of the most powerful tools in business – and one of its most potent threats,” said John Gill, JD, President of the ACFE. “Awareness is our best defence as new risks continue to evolve. Educating professionals, equipping government and industry and empowering the public to recognise the AI-guided threats proliferating unseen is vital to maintaining trust and building confidence for what lies ahead.”

For anti-fraud professionals, the ACFE and SAS offer the Fraud Week webinar, Agentic AI in Action: Intelligent, Adaptive Fraud and Financial Crime Prevention, available on demand.

“AI is blurring the boundary between truth and imitation, with untold billions at stake,” said Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions at SAS. “Even as AI drives seemingly limitless progress, it tests the very limits of truth itself. We must educate the public about what’s at stake – and ready government and industry to face AI-charged fraud, at a time when fewer than one in 10 anti-fraud pros feel well prepared, according to our recent survey of ACFE members.”

Follow #FraudWeek on LinkedIn and other social channels to join the global conversation. Experts from SAS and across the anti-fraud community will share practical tips, research highlights, use cases and other resources throughout the observance.

Confronting AI-driven fraud across industries

Across sectors, AI is reshaping the scale and sophistication of fraud – and the tools and strategies required to stop it. To strengthen safeguards, SAS offers these field-proven resources:

    AI in banking: Adversary – and hero. Scams are evolving fast, but so is banks’ ability to stop them. The Future of Trust: How AI is Powering a New Era in Banking Fraud Detection reveals how advanced analytics helped one bank cut alerts by 40% and improve detection by 35%, fortifying both safeguards and customer experience.
    AI in public sector: Signal – and shield. Even as schemes targeting government programmes grow more complex, detection tools advance in parallel. The Public Sector Fraud-Fighting Maturity Assessment, based on industry research by Coleman Parkes and SAS, helps agencies benchmark their capabilities and identify gaps to advance AI-amplified, anti-fraud readiness.

From digital identity verification to real-time transaction monitoring to insurance claims analysis to payment integrity in public benefits, organisations worldwide are strengthening their defences with SAS. These examples showcase how businesses and government agencies are using advanced analytics to outmanoeuvre AI-scaled fraud threats.

Powering faster, smarter decisions with high-trust identity verification

BankID, a service from Stø, is Norway’s national digital identity provider, securing authentication and digital signing for more than 4.6 million citizens across banking, government and private-sector platforms. Processing nearly a billion transactions each year, BankID is modernising its infrastructure – transforming decades of trusted identity data into real-time, self-learning intelligence.

To strengthen its defences against AI-age threats, BankID is integrating high-trust identity and authentication signals (e.g., login patterns, device metadata, signing behaviour, etc.) into SAS’ real-time fraud scoring and decisioning systems. This convergence of ID assurance and behavioural analytics drives earlier anomaly detection, greater risk-scoring accuracy and stronger protection against account takeover and synthetic identity fraud.

“BankID goes beyond identity protection – it powers intelligent fraud prevention,” said David Sæle, Product Manager, BankID Anti-Fraud. “By combining our identity signals with SAS’ AI-driven fraud analytics, we’ve moved from reacting to fraud to anticipating it. The result is smarter real-time detection and fewer false positives, enabling faster, more confident decisions that protect both users and trust at a national scale.”

Transforming real-time transaction monitoring with machine learning

Ajman Bank is a Sharia-compliant bank serving retail, business and government customers in the United Arab Emirates. The fast-growing financial institution has allied with SAS and regional integration partner DataScience Middle East to advance its fraud detection capabilities and raise the regional standard for modern fraud prevention.

Ajman Bank has deployed SAS’ real-time fraud management and decisioning platform to monitor activity across cards, payments and digital services. Machine learning models evaluate and score customer behaviour in real time, reducing false positives and helping investigators focus on the highest-risk threats. By unifying data and analytics across channels, the bank is building a faster, more precise defence against evolving fraud tactics.

“Our partnership with SAS and DataScience ME reflects our commitment to adopting world-class technologies that protect our customers and ensure the integrity of our banking operations,” said Abhishek Sharma, Chief Risk Officer at Ajman Bank. “With real-time analytics and tailored models, we are delivering smarter, safer banking for our community.”

Mapping hidden insurance fraud networks at scale – a first of its kind in Korea

Among South Korea’s major general insurers, DB Insurance serves more than 10 million customers and manages millions of claims annually. Confronted with organised criminal rings that linked crooked repair shops, medical clinics and brokers, the insurer partnered with SAS to develop Korea’s first AI-powered fraud detection network.

Built on SAS Viya, the DB T-System unifies decades of policy, claims and customer data on a single platform. Then, applying network analytics, the system exposes hidden relationships and patterns of deceit across millions of claims, refining its precision with each investigation. As a direct result, detection accuracy leaped 99%, while analysis time dropped from hours to mere minutes per case. The team now processes 30x more cases than before – not only curbing fraud losses but accelerating claims resolution.

“We turned it on, and just like that, dozens of cases lit up revealing invisible fraud connections,” said a senior claims operations leader at DB Insurance. “We could see 10 million customers, every claim, every connection. We stopped reacting to fraud and started preventing its spread.”

Advancing payment integrity in public benefits with machine learning

Since 2019, one large southern US state has teamed with SAS to strengthen the integrity of its food assistance programme. What began as workflow automation for portions of the state's SNAP benefit recovery process has since expanded statewide. Using data captured in early stages of the collaboration, SAS later created a machine learning model to risk-score overpayment referrals and help programme staff prioritise cases for investigation, which must meet strict timeliness standards.

Building on this progress, officials expanded that model to evaluate all active SNAP cases across the state and flag those with the highest likelihood of error. The state now uses SAS Payment Integrity for Food Assistance to guide and sharpen its review processes, directing attention to higher-risk cases – a model of how advanced analytics can enhance responsible public programme oversight.

“We saw a 50% reduction in the processing time for our investigations, and we moved from a 12-month processing time down to six months,” said a senior official responsible for the state’s programme integrity oversight. “That was huge for us, because we were also facing budget constraints and couldn’t add more resources.”