2025 did not deliver one defining compliance moment. Instead, it left behind a series of small signals that, taken together, point to a much larger shift underway in financial services compliance.
This article breaks down the most important lessons from 2025 and connects them to the strategic decisions compliance leaders will likely face in 2026, offering a perspective on where attention, investment, and governance will matter most.
2025 in review: Key compliance takeaways
AI moved into daily operational infrastructure
In 2025, AI transitioned from experimental use to everyday application. Compliance teams began using AI-driven summaries, automated classification, policy tagging, and early-stage risk scoring at scale across voice and text communications.
At the same time, cracks appeared. Many firms struggled to explain AI outputs at audits, recreate decisions made with AI assistance, or even prove when and how an AI suggestion influenced an outcome. On top of this, alerts were often generated without proper logging; decisions lacked explainability.
The lesson we should take from this is not that AI has failed, but that AI without governance fails compliance.
Regulations tightened, governance expectations rose
Regulators caught up quickly with technology adoption. Frameworks such as DORA and global cyber governance rules made it clear that technology choices are now regulatory choices.
For example, cloud strategy became inseparable from compliance strategy. Data residency, outsourcing risk, and operational resilience moved from procurement checklists into board-level discussions.
In parallel, regulators pushed harder on cyber incident disclosure. In the United States, for instance, major incidents now require notification within tight timelines, and banking regulators expect transparency well beyond minimum public reporting.
The lesson: Compliance teams learned that their governance frameworks must move as fast as technology adoption.
Data became the limiting factor
Despite years of digital transformation, many firms discovered they did not have the data foundation required to support advanced AI responsibly, let alone at scale. Communications data remained largely structured and siloed across systems, departments, and regions. This meant that training and tuning AI models internally proved difficult if not impossible and limited the likelihood of reaching parity with established open-source or public models.
The lesson: The bottleneck was not algorithms, but data access, quality, and governance. For AI to be truly effective and defensible, compliance teams must ensure that data is complete and accessible across the enterprise. This includes breaking down silos, standardizing formats, and implementing clear retention, logging, and audit policies.
Consolidation exposed integration risk
Industry consolidation and platformization accelerated in 2025, creating new compliance challenges. Mergers and acquisitions exposed integration gaps, inconsistent policies, and cultural misalignment. While platform strategies promised efficiency, they raised concerns around third-party dependency, data governance, and regulatory alignment.
The lesson: While rapid growth or consolidation is not inherently dangerous, governance is key to avoid risks. To manage this effectively, organizations must invest in structured integration planning, continuous oversight of platform dependencies, and robust cross-functional collaboration.
2026 predictions: Where compliance is headed
Compliance & non-compliance recording will converge
Businesses have come to realize that the same infrastructure that satisfies regulators can also drive efficiency across sales, training, quality assurance, and operations. As a result, the distinction between compliance recording and non-compliance recording will continue to blur, with audio, video, and text capture converging into unified platforms.
This convergence is not just about cost, but about simplification, governance, and data availability across the organization.
Real-time will be the new standard
The biggest shift ahead is latency. Compliance is moving from a review-it-later to an act-on-it-now approach. Financial institutions will deploy real-time and near-zero-latency AI models to support live decision-making across calls, chats, and traditional systems like email.
This could include:
- Real-time automated Know Your Customer (KYC) and Anti-Money Laundering (AML) case handling
- Continuous surveillance with autonomous alert triage
- Live deepfake detection embedded directly into call center and compliance workflows
This continuous risk monitoring is already starting to replace periodic reviews, enabling financial crime (which is increasingly synthetic and AI-driven) to be detected mid-call, rather than days later.
That said, none of this works without access to live data streams, making compliance capture vendors critical enablers. At the same time, integrations between capture platforms and internal systems remain a key bottleneck.
Explainable AI will be a non-negotiable principle
Regulators are converging on the same message: If your AI cannot be explained, it is a liability. The EU AI Act states that every alert, dismissal, and decision must be traceable, documented, and auditable.
By August 2026, high-risk AI systems, which include credit scoring and surveillance tools, must meet strict explainability and documentation requirements. While this does not mean everything needs to be built in-house, it does mean asking harder procurement questions and walking away when answers fall short. The days of defending decisions with “the model flagged it” are over.
Firms will shift toward AI they can control
Explainability pressure will also reshape vendor strategies. Firms will increasingly prefer AI models with configurable detection logic, transparent version histories, and visibility into training data, rather than opaque “black box” systems.
Shadow AI will emerge as a notable risk. While AI can be immensely valuable, unsanctioned AI usage outside approved systems and without audit controls creates visibility gaps that compliance teams will have to address.
The AI surveillance officer will emerge
With AI becoming embedded in surveillance, the AI surveillance officer will emerge as a critical role in many financial services firms. Responsibilities will likely include:
- Reviewing AI-generated alerts: Speech and text analytics platforms scan recorded calls, emails, chats, and other communications for red flags like potential market abuse, insider trading, mis-selling, unauthorized advice, etc. The surveillance officer will review these alerts to determine if they represent genuine issues or false positives.
- Tuning and calibrating models: Work with vendors or internal teams to refine the AI's lexicons, thresholds, and detection rules to reduce noise and catch real misconduct.
- Escalating confirmed issues: When genuine violations are detected, officers will escalate to compliance leadership, legal, or regulators as required.
- Documentation and reporting: Maintain audit trails and produce reports for regulators demonstrating the firm's surveillance program is effective.
This new role requires investigative instinct, technical fluency, and regulatory awareness—a combination that is currently rare. Demand will likely outpace supply.
Compliance adaptability will be critical
AI is accelerating financial crime, including deepfake fraud; geopolitical instability is pushing Europe to invest in regional alternatives and rethink cross-border dependencies; regulations continue to evolve, often faster than operating models.
Meeting these demands will require more than reactive change. It requires what might be called compliance apperception: The ability to absorb new regulatory and technological signals, interpret them through past experience, and incorporate new capabilities quickly and responsibly.
With strong data governance and AI controls, compliance can move from a reactive function to a predictive one. Behavioral compliance, risk awareness, and proactive intervention will differentiate organizations in regulatory outcomes and customer trust.
Conclusion: Closing the gap between risk & control
The compliance landscape entering 2026 is faster, more connected, and less forgiving than ever. What stands out most is not any single regulation or technology trend, but the growing gap between how fast risk evolves and how slowly many control frameworks are able to adapt.
In 2026, compliance strategies will be judged less on policy completeness and more on execution. Can decisions be explained? Can risks be identified early enough to matter? Can governance keep pace with AI-driven processes without becoming a bottleneck?
Financial services businesses that treat compliance as a strategic capability, rather than a reactive obligation, will be better positioned to manage regulatory scrutiny, operational resilience, and customer trust in the year ahead.
