Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Sean Hogan, Director of Business Development, Bridgenext
Artificial intelligence has moved far beyond the experimental phase in financial services. As we enter 2026, AI is now deeply embedded in how institutions make decisions, detect risk, support customers, and manage operations. One of the clearest indicators of this shift is the growing number of financial companies listing AI as a material risk in their 10-K filings. This signals a major turning point: AI is no longer treated as a niche technology; it is now a regulated operational system with direct implications for financial performance, regulatory exposure, and customer trust. As regulatory expectations evolve and AI agents become more capable, financial institutions will need to rethink compliance as a continuous, embedded discipline shaping everyday business decisions.
AI Governance Becomes a Strategic Imperative
The acknowledgment of AI as a risk in formal financial disclosures highlights how central AI has become to core business functions. Whether AI is contributing to credit decisions, trading models, fraud detection, or customer interactions, regulators increasingly view AI-generated outputs the same way they view decisions made by human employees. This means organizations must expand governance models to ensure that AI systems not only perform well, but also comply fully with existing regulations such as GLBA, fair lending requirements, AML rules and SEC guidance.
As this shift accelerates in 2026, several priorities will emerge. Organizations will need to build clear documentation around how AI is used across risk-sensitive workflows and establish strong governance structures that define ownership at both the business and technical levels. Boards will become more involved, treating AI oversight with the same seriousness they apply to cybersecurity. Investors and regulators will expect transparency into how AI systems are built, monitored and controlled, including explainability, model lineage, and version management. In short, AI governance will evolve from an IT-led initiative into a business-critical priority.
AI Audits Will Redefine Regulatory Expectations
Just as cybersecurity audits and SOC assessments became standard over the past decade, AI audits are poised to follow the same path. As AI systems influence underwriting, fraud prevention, surveillance, reporting, and customer service, regulators will require verifiable assurance that these systems are safe, accurate and well-managed.
The scope of AI audits in 2026 will likely expand in several key areas. Auditors will examine how models are validated, how performance drift is detected, and whether explainability is appropriate for the use case. Data provenance will become central, with auditors requiring a deep understanding of where training data comes from and whether its use aligns with privacy and consent regulations. Bias and fairness assessments will be expected for lending and fraud-related applications, while red-team testing will be used to evaluate model robustness against prompt injection, manipulation, or misuse.
Vendor risk will also stand out. Many institutions rely heavily on third-party AI models, which means auditors will extend their reviews to include vendor documentation, SLAs, safety practices, and model update procedures. Over time, the industry may see the emergence of standardized AI SOC-style reports from vendors, further formalizing the ecosystem.
Compliance Transitions from Reactive Review to Always-On Oversight
The dynamic nature of AI introduces challenges that traditional compliance processes cannot accommodate. Unlike static models, AI systems update, adapt, and may drift over time. This makes after-the-fact compliance review insufficient. In 2026, financial institutions will increasingly move toward continuous monitoring practices designed to provide real-time visibility into how AI systems behave.
These approaches will include logging prompts and outputs, tracking model versions and upgrade histories, monitoring the data used for fine-tuning or RAG, and detecting anomalous or unexpected results. Automated alerts will become more common for high-impact outputs such as credit decisions or fraud scoring changes.
Financial Institutions Turn to AI to Keep Up With Expanding Regulation
The pace of regulatory change has outstripped what manual compliance processes can manage. AI will play a larger role in monitoring new regulations, analyzing differences across geographies, and supporting compliance teams by identifying potential gaps or conflicts. In high-impact areas like AML, fraud detection, and fair lending, AI will continue to strengthen monitoring capabilities by identifying new patterns, reducing false positives, and supporting more effective investigations. These tools do not replace human judgment, but they act as powerful diagnostic layers, helping institutions maintain compliance amid growing regulatory complexity.
Human-in-the-Loop Becomes Non-Negotiable in Regulated AI
As AI agents become more sophisticated, some sectors have explored allowing them to take autonomous actions. However, in financial services – a highly regulated environment – AI should not independently make financial decisions or offer financial advice. Tools like Salesforce’s Agentforce Agents demonstrate how AI can support information gathering and assist with customer questions, but final decisions must remain in the hands of licensed employees.
In the next year, the industry will double down on human-in-the-loop structures. AI will streamline case preparation for disputes, analyze account information, and surface insights for employees and customers, but the responsibility for approving or executing decisions will stay with humans. This structure not only satisfies regulatory requirements but also reinforces trust in automated systems.
Transparency Will Become a Competitive Advantage
As AI moves closer to customer-facing workflows, transparency will play a growing role in compliance and trust-building. Financial institutions will face greater expectations to explain when decisions are automated, how customer data is used in training, and what recourse exists when AI-driven outcomes are disputed. This mirrors the evolution of cybersecurity governance, where disclosures and consumer protections have become standard. In 2026, transparency in AI practices will shift from a regulatory obligation to a differentiator for institutions seeking to build long-term customer confidence.
The Road Ahead
The year ahead will reshape how financial institutions think about compliance and accountability as AI becomes woven into routine decision-making. Rather than treating AI as a peripheral tool, organizations will need to manage it with the same rigor applied to other critical systems. Those that rise to this challenge will be able to deploy AI with greater assurance, achieving meaningful progress while maintaining the confidence of regulators, investors and customers.
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ABOUT THE AUTHOR
Sean Hogan is Director of Business Development at Bridgenext, where he helps banks and financial services organizations modernize operations and customer experience through Salesforce-driven automation and AI. With more than 35 years in technology and nearly 15 years working within the Salesforce ecosystem, Sean brings deep expertise in retail banking, wealth management, and highly regulated environments. He focuses on helping institutions simplify complex processes, identify high-value automation and agentic AI use cases, and balance innovation with regulatory and customer trust requirements.
