PwC’s Banking & Capital Markets Advisory Leader, Sean Viergutz, Speaks about Banking and AI

Erik Vander Kolk, CEO of Banking Exchange, Interviews PwC’s Banking & Capital Markets Advisory Leader Sean Viergutz

1. Two years ago, banks said they were intending to use AI for a number of purposes, and last year there seemed to be an uptick. What do you think the progress has been last year and heading into Q2 in 2026?
   
   Over the past year, banks have moved from experimentation to scaled deployment, particularly in software development, service operations, and risk workflows. What changed was not just familiarity, but the ability to implement AI within stronger governance and security frameworks while demonstrating measurable ROI. Heading into Q2 2026, leading institutions are shifting from isolated use cases to redesigning end-to-end processes with AI embedded into daily operations. The focus now is increasingly on operating model transformation rather than incremental efficiency gains.
   
   
2. What are the key areas where banks are going deeper with AI use beyond just operational efficiency: Fraud prevention? Credit modeling? Where do you see the biggest transformation coming?
   
   Banks are going deeper in financial crime, fraud detection, credit decisioning, and customer personalization because these areas directly impact revenue, losses, and capital allocation. One of the most significant transformations we’re seeing is where AI improves decision quality at scale—reducing false positives, accelerating onboarding, and enhancing credit risk assessment. We also see increasing application of AI in proactive customer engagement and next-best-action capabilities. A major long-term shift will occur as AI moves from insight generation to workflow execution.
   
   
3. How are banks approaching proprietary versus licensed AI models? How is it different based on bank size? Under $10 billion, up to $60 billion and larger? How can smaller banks leverage AI to compete with the larger financial institutions? Or can they?
   
   Larger institutions typically pursue a hybrid strategy—leveraging licensed frontier models while differentiating through proprietary data, domain tuning, and internal controls. Mid-sized and smaller banks are more likely to adopt vendor-based solutions to accelerate time to value and manage costs. Institutions under $10bn and up to $60bn in assets generally compete by focusing AI investment on a small number of high-impact use cases rather than building foundational models from scratch. Smaller banks can compete effectively if they are disciplined about prioritization and integration, though scale advantages remain meaningful.
   
   
4. Can you share with us some uses cases where you know of banks that are really using it across bank operations and not just in silos?
   
   While we can’t name specific companies, we are seeing banks making meaningful progress deploying AI across shared platforms rather than just in functional silos. This typically includes a common data layer, centralized governance, reusable AI components, and embedded controls across service, risk, operations, and technology. We are seeing end-to-end workflows, such as onboarding, KYC case management, or service resolution, augmented by AI from initiation through documentation and quality assurance. The differentiator is process redesign and platform reuse rather than model sophistication alone. Other areas include finance processes and engineering, spanning code generation, QA/QC, and testing.
   
   
5. Who are the stakeholders at banks that need to be involved when it comes to widespread AI Adoption?
   
   Successful AI adoption requires strong executive sponsorship—often from the COO, CIO, or Chief Data Officer—to align funding, governance, and cultural change. Adoption also requires coordination across business leadership, technology, data and AI teams, information security, model risk management, compliance, legal, and operations. Model validation and compliance functions play a particularly important role in regulated use cases such as credit and financial crime. Without cross-functional ownership, adoption tends to remain siloed.
   
   
6. How will digital currencies and stablecoins impact banks in 2026 and 2027? Are banks ready?
   
   Digital currencies and stablecoins present both competitive pressure and strategic opportunities for banks. They could impact deposits and payment economics while enabling faster, always-on settlement models. Readiness varies across the industry, with larger institutions generally further along in evaluating integration, custody, liquidity management, and compliance considerations. Banks that succeed will be those that incorporate regulated digital money capabilities without disrupting core funding and risk frameworks.