The AI experiments are over. In 2026, banking enters a new phase, one where autonomous agents handle real customer requests, synthetic data threatens core repositories, and trust becomes a measurable performance metric. The question is no longer whether AI will transform banking, but whether institutions are prepared for the consequences of the accelerating transformation already underway.
From agentic commerce disputes to quantum-powered risk modelling, SAS experts offer a ‘banker’s dozen,’ 13 industry-defining predictions that will separate institutions that master intelligent banking from those still struggling with the basics. Here is what is coming and what banks need to know now.
Verified intelligence becomes the new currency of trust
“AI has made financial institutions faster, smarter and infinitely more confident, sometimes too confident. From credit scoring to fraud detection to customer service, we have trained intelligent systems to decide in milliseconds, but has the industry risked losing sight of its most human principle along the way? Trust must be earned, not assumed,” says Alex Kwiatkowski, director of Global Financial Services, SAS.
“In 2026, trust will morph from a promise to a performance metric as banks shift from model-driven to proof-driven intelligence. Demanding verifiable transparency across every prediction, decision and interaction will become the new standard of intelligence. In other words, do not trust this prediction – until you can prove it.”
Agentic AI graduates from promise to production
“2026 will mark the dawn of agentic AI in banking as semiautonomous systems begin to take on meaningful work across the enterprise. The future of intelligent banking will be shaped by AI-driven agents that manage customer requests, orchestrate workflows and make governed, explainable decisions at scale. This shift will fundamentally change how banks design operations and measure the value of AI,” says Diana Rothfuss, global solutions strategy director for Risk, Fraud & Compliance Solutions, SAS.
According to IDC, financial services firms will spend more than $67 billion on AI by 2028. Production deployments tied to decisions and operations are poised to see the biggest growth. The industry has matured beyond the proof-of-concept, and the banks that succeed will be those that industrialise their AI to turn pilots into profit and governance into competitive advantage.
Banks inherit the fallout of robo-shopping.
“From call centres to the C-suite, financial institutions will be forced to face the consequences of the rapidly expanding agentic commerce economy. Banks will see a surge in disputes triggered by autonomous AI agents making purchases that the customer never approved, and fraud teams will face new risks as criminals learn to hijack or mimic legitimate agents,” according to Adam Neiberg, global banking senior marketing manager, SAS.
As agentic e-commerce grows, banks must learn to authenticate, not only people, but also the AI agents acting in their name, adding a new layer of complexity to an already tough fight against financial crimes. Frameworks such as agentic tokens, behavioural signatures and dynamic risk scoring represent the first wave of controls banks will need to safeguard their human customers and their bottom line.
Banks erect data purity ‘vaults’ amid synthetic data contamination
“Banks will confront a new kind of data integrity crisis as generative AI and synthetic data seep into core repositories in ways that are difficult to detect. Unlike the isolated data quality issues of the past, GenAI can introduce errors at scale and with a level of realism that makes contaminated data extremely hard to surface,” warns Ian Holmes, director and global lead for Enterprise Fraud Solutions, SAS.
As financial institutions experiment with synthetic data to accelerate model development, many will unknowingly introduce subtle biases and distortions into credit, fraud and risk decisioning pipelines. To protect critical workflows, banks will begin securing their golden source data in controlled digital vaults and impose stricter governance on how GenAI tools can interact with core data sets.
Unlock the potential of unstructured data
“In 2026, generative AI will become, for unstructured data, what traditional statistics has long been for structured data, giving banks the ability to extract meaning and insight at scale. More than 80% of enterprise data is in unstructured formats such as text and images, and this volume is growing by 50% to 60% each year,” states Terisa Roberts, global director for risk modelling, Decisioning and Governance, SAS.
Banks will begin adopting knowledge agents powered by large language models and retrieval-augmented generation to turn previously underused unstructured data into quick, actionable answers. They will use these new insights to accelerate strategic business decision-making and transform risk management into a more proactive, intelligence-driven discipline.
Romance scams get an agentic upgrade
“Your chances of dating a model have never been higher, a large language model, that is. While AI-powered romance scams already exist, they will surge to record levels as fraudsters weaponise emotional deception at scale. What once required weeks or months of hands-on engagement can now be automated and accelerated with minimal effort,” warns Stu Bradley, senior vice president of Risk, Fraud and Compliance Solutions, SAS.
As machine-assisted manipulation advances, the line between genuine connection and synthetic seduction will blur further, testing not only fraud defences, but human intuition itself. Financial institutions will be pressed to act as emotional firewalls for their customers, combining behavioural analytics and AI-driven monitoring to detect exploitation patterns before the monetary damage is done.
AI investment pressures trigger a shakeup in financial crime technology
“The anti-financial crime compliance market will undergo a major shakeup in the year ahead as vendors struggle to embed advanced AI into their offerings. Recent divestitures underscore the scale of reinvestment required to modernise dated, rules-based platforms, leaving many banks with tools that cannot keep pace with evolving fraud and money-laundering threats. As the difficulties of bolting AI onto legacy platforms come to light, financial crime technologies built natively on AI platforms will shine brightest,” says Beth Herron, Americas lead for Banking Compliance Solutions, SAS.
In 2026, financial institutions will accelerate the adoption of cloud-native, AI-driven AML (anti-money laundering) and fraud solutions that can surface complex patterns. Our latest survey of ACAMS (The Association of Certified Anti-Money Laundering Specialists) members shows that most institutions already see AI as essential for AML modernisation, and banks that migrate toward explainable, real-time analytics will gain significant compliance and risk advantages.
AI and quant credit will accelerate bond market efficiency
“The growth of quantitative credit strategies will accelerate price discovery in corporate bond markets, catalysed by AI-assisted models that rapidly incorporate new information, alternative data and forward-looking credit indicators. Active fixed income teams will move beyond ratings-centric workflows and adopt flexible, ML-driven modelling and decisioning infrastructure that translate diverse signals into trading decisions,” states Stas Melnikov, head of Quantitative Research and Risk Data Solutions, SAS.
“Strong data governance and rigorous model risk management will be the necessary ingredients for this process and technology evolution. Additionally, innovation in credit rating risk modelling will help investors reduce losses and capture opportunities.
Bubble-aware risk management should become standard practice … but won’t
“In 2026, leading banks and asset managers will start embedding bubble-aware models into pricing, ALM and stress testing. These models will explicitly break down the market price of assets into their fundamental drivers, while also examining risk premiums and transient bubble components. Bubble-aware models help firms recognise factors that cause asset prices to rise sharply and unsustainably. While these models should become part of all banks’ standard practice in 2026, I fear – and predict – they will not,” notes Robert Jarrow, advisor and industry consultant, Quantitative Research and Risk Data Solutions, SAS.
Stablecoins move from theory to practice
“Imagine a US-EU corporate corridor that settles in minutes rather than days. We are not there yet, but the year ahead will see regulated stablecoins move into real banking pilots. With clearer frameworks in the US and EU, banks will begin testing stablecoins for cross-border settlement and treasury use, leveraging their inherent benefits: faster fund movement, lower costs, and greater transparency. Some banks will also explore tokenised deposits or partnerships with licensed issuers to move money on digital rails with stronger auditability and compliance. These early pilots signal the first meaningful step toward modernising international payments,” predicts Ahmed Drissi, AML lead for Asia-Pacific, SAS.
Retail banks shift from testing commerce media models to scaling them
“By the end of 2026, every major retail bank will have a media strategy, whether they call it that or not. Banks that quietly tested the model over the past 12 to 18 months will begin reporting measurable revenue gains as advertisers and brands recognise the power of verified financial data. Institutions that operationalise financial media networks could realistically see a 20% to 30% uplift in non-interest income within two years,” adds Cornelia Reitinger, Head of Advertising Business Development, SAS.
Banks embrace climate risk stress testing
“As the impact of storms, wildfires and droughts on bank portfolios intensifies worldwide, banks face mounting pressure from customers, regulators and shareholders to improve their climate risk management efforts; 2025 saw the first-ever fine for a bank’s noncompliance with climate risk regulations. Therefore, I foresee banks stepping up their climate risk stress testing to close gaps in modelling, governance and infrastructure. Its closer integration with banks’ core business-as-usual risk management frameworks will be essential to effectively respond to increasing pressures,” forecasts Peter Plochan, EMEA principal risk management advisor, SAS.
AI-driven automation and integration of stress testing processes will be critical enablers, not only for addressing climate risk requirements, but also for other emerging scenario analysis use cases, such as the European Central Bank’s recently announced geopolitical risk reverse stress test.
Banking takes a quantum leap
“This will mark the year we see the first impacts that hint at how quantum AI will reshape the banking landscape through the end of the decade. Hybrid quantum-classical computing will move from pilots to production, delivering breakthroughs in risk and fraud, and it will expand the frontier of how banks optimise, simulate and decide, especially in areas where classical models degrade. Banks building early experience will see transformative gains in accuracy, agility and performance that deliver an outsized edge over the competition,” states Julie Muckleroy, global banking strategist, SAS.
Explore predictions, discover solutions
AI’s impact on banking extends far beyond these 13 predictions. Explore more 2026 technology trends across industries here.
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