From prediction to action: What 2026 financial services industry trends could mean for the future of internal audit

Talking points

• The mindset at many financial services industry (FSI) organizations is expanding to include not only hot pursuit of the next innovation but also building the governance and assurance infrastructure to deploy it responsibly.
• The transition from traditional assurance to continuous monitoring, adaptive controls, and integrated governance is now table stakes.
• Meaningful internal audit transformation in FSI means evolving from periodic checkpoint to real-time strategic adviser.

The race to adopt the next big technology used to follow a familiar script: identify the trend, understand it, and deploy it before competitors. From agentic artificial intelligence (AI) to stablecoins to smart contracts, the practice has been to move fast and figure the details out later. But this year, something changed.

Deloitte’s annual FSI Predictions 2026 report from the Deloitte Center for Financial Services surfaces the innovations reshaping how we bank, invest, insure, and transact—expanding consumer access, cutting costs, and reimagining user experiences. What’s different is what FSI organizations are doing with that intelligence.

The transition from innovation to value

Rather than rushing to operationalize the latest innovation before competitors, many FSI leaders have shifted their focus to the foundation: building a robust risk management, governance, controls, and audit backbone capable of supporting new technology—faster, more responsibly, and with greater resilience. The question is no longer “what’s the latest technology trend?” It’s “do we have the infrastructure to deploy it well?”

Emerging risk areas require a different kind of controls environment

Fast-moving technology like AI continues to challenge existing risk, controls, and assurance environments. FSI Predictions 2026 profiles agentic AI use cases across wealth management, life insurance, and banking that can enhance advice delivery, expand access to underserved clients, and unlock new revenue streams. What these systems share: they’re adaptive, generating probabilistic outputs that can shift over time through model drift—sometimes without human intervention.

Scaling AI typically requires real-time monitoring, continuous testing, and adaptive controls for evolving AI models—a process known as AI model risk management. AI assurance should also provide human oversight of inputs and outputs, monitor AI model drift and bias, document change approvals for prompts and guardrails, and provide audit evidence grounded in recognized frameworks. When this infrastructure is in place, assurance becomes a live system for leaders and regulators—not after-the-fact documentation.

Keeping pace with regulatory frameworks

Emerging regulatory frameworks are another consideration. Enforcement of major AI frameworks—including the European Union AI Act—begins this year, requiring organizations to demonstrate how they govern AI, not just deploy it. SEC cybersecurity rule changes bring AI systems impacting financial reporting into scope, while FSI-specific rules continue to emerge. Meanwhile, financial organizations are asking for attestation over AI systems. The message is clear: Your governance, testing, and reporting need to operate at the speed of AI.

Implications for audit and assurance

Legacy internal audit, risk, and compliance frameworks were built on periodic reviews—a model that likely won’t work with technology that never stops changing. To potentially meet the challenges of AI, internal audit must shift from backward-looking to forward-looking assurance. This approach should include continuous, real-time monitoring capable of detecting AI-related issues much earlier than periodic audits. 

By helping organizations design proportionate risk-based AI assurance frameworks, internal audit can also elevate its role as a strategic adviser. It can become a voice at the table from the start, helping build innovative, well-controlled AI assurance instead of serving as a final checkpoint.

What leading FSI organizations are doing differently

FSI leaders scaling innovative technology aren’t just investing more—they’re operating differently, by:

• Investing in data-driven controls and automation. Legacy technology and fragmented data architectures—not designed for real-time execution or continuous decisioning—often create structural barriers to deployment in FSI. Organizations modernizing with cloud-based architectures and unified data platforms may be better positioned to deploy at scale.
• Scoping risk management by materiality. Prioritizing high-impact risks over comprehensive coverage can keep scope manageable and direct resources where they matter most.
• Building integrated risk management across functions. AI systems don’t respect organizational boundaries; a single model can touch credit risk, compliance, IT security, and financial reporting at the same time. When those functions operate in silos, oversight gaps are inevitable. FSI leaders are breaking down silos by establishing shared risk taxonomies, common data environments, and cross-functional governance forums that give leadership a unified, real-time view of AI risk.
• Aligning internal audit, risk, and compliance under a cohesive operating model. Leading organizations are moving beyond sequential handoffs to a coordinated assurance model with shared frameworks, governance forums, and coordinated reporting cycles. The result can be less duplication, faster assurance, and a clearer picture of risk for leaders and regulators.

Leading practices you can adopt today

The shift toward AI-driven operations is underway. FSI organizations that act now will likely be better positioned to scale, satisfy regulators, and turn governance into a competitive advantage. Consider these steps:

• Reassess control frameworks against the current risk landscape. Controls built for rules-based processes may not be enough for AI, which can make changes or unexpected decisions. Review and update those controls—before gaps create problems.
• Identify gaps in AI and technology governance. Most governance frameworks predate AI as a core operational tool. Mapping AI use against policies can identify coverage gaps before they lead to control failures, operational disruption, or unintended outcomes.
• Align audit plans to emerging trends. Audit plans anchored to last year’s risk profile may look in the wrong direction; forward-looking indicators that reflect the latest technology, financial, and risk trends can keep assurance aligned with today’s risks.
• Take a more targeted, value-driven approach to risk oversight. Broad coverage often consumes resources without proportionate return; focusing on the highest-risk areas can sharpen insights and demonstrate internal audit’s strategic value.

What role can Deloitte play?

Deloitte’s Audit & Assurance professionals work with FSI organizations to modernize risk, controls, and governance frameworks. From assessing technology governance gaps to building continuous monitoring capabilities, our services help clients move faster—with greater confidence. Contact us to start the conversation.

For further reading, view our FSI Predictions 2026 report or explore our audit and assurance services for the financial services industry.

Deloitte’s Audit & Assurance AI leadership 

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