The New Compliance Trinity: Regulations, AI and Trust

A Structural Shift in Regulatory Expectations

For decades, regulatory compliance within financial institutions has been defined by a familiar rhythm: interpreting new rules, updating policies, remediating gaps, and responding to supervisory findings. This reactive model, while imperfect, was largely sufficient in a time when regulatory change was less continuous, less digitized, and less tightly coupled with technology risk, which was less substantive.

That model is no longer fit for purpose.

The convergence of evolving regulation and its scrutiny, the increasing use of artificial intelligence (AI), and heightened expectations around trust havr driven the emergence of a new compliance operating model. Regulators are no longer focused solely on whether controls exist, but whether they are embedded, operational, and demonstrably effective at scale. In this environment, reactive compliance can itself become a material regulatory and operational risk.

For compliance and risk officers, the following challenges are quickly expanding: 

• Governing advanced AI systems and managing technology risk
•  Maintaining trust with regulators, clients, and counterparties simultaneously

Regulations Evolve from Static Rules to Continuous Oversight

Global regulatory frameworks are undergoing a fundamental evolution. Regulators in various regions are shifting from static guidance to enforceable requirements for ongoing oversight, transparency, and accountability.
 
AI-specific regulation and supervisory guidance—such as the European Union’s Artificial Intelligence Act—are reshaping regulatory expectations. This shift is reinforced by influential voluntary frameworks such as the US National Institute of Standards and Technology’s AI Risk Management Framework, and by the growing integration of AI oversight into prudential, conduct, and data protection regimes worldwide.

Regulators increasingly expect institutions to demonstrate:

• Clear ownership and accountability for AI-driven outcomes
• Risk classification and impact assessment of automated systems
• Ongoing monitoring, testing, and post-deployment controls
• Audit-ready evidence of how decisions are governed and, where required, explained

These expectations extend beyond technological teams. Boards, senior management and control functions are being held accountable for the governance, oversight and control frameworks governing how AI is deployed across regulated activities such as onboarding, know your customer (KYC), anti-money laundering (AML), and client lifecycle management.

AI as a Tool and a Risk

Artificial intelligence, particularly generative AI and increasingly autonomous, agent-based systems are gradually moving from experimental use cases to production within financial institutions. This is evident in compliance heavy functions, where AI is used to automate onboarding, detect financial crime, assess risk, and manage complex data sets.

While the efficiency gains are substantial, AI introduces a distinct class of regulatory risk. Unlike traditional rules-based systems, AI systems can:

• Learn and evolve over time
• Produce probabilistic rather than deterministic outcomes
• Operate at a scale and speed that outpaces human review

This creates new challenges for explainability, traceability, accountability and control. Regulators have made it increasingly clear that these challenges cannot be adequately mitigated through policy statements or ethical principles alone. They require operational governance embedded directly into AI systems and workflows.

Artificial intelligence has evolved beyond a supporting operational capability and now constitutes a component of regulated activities where it is used in decision-making or control processes that must themselves be governed, controlled, and overseen within the compliance ecosystem.

Trust: The Currency of Modern Compliance

Trust has always underpinned financial regulation, but its role has intensified in the age of AI. Regulators, clients, and counterparties increasingly expect institutions to prove that their systems operate fairly, transparently, and within defined risk boundaries.

Trust today is evidence-based. It is established through:
•    Explainable decision-making that can be reviewed and challenged
•    Robust audit trails which demonstrate how outcomes were reached
•    Clear human accountability and oversight over automated processes
•    Strong data protection, privacy, and security controls

Where trust is eroded, remediation costs escalate, supervisory and other stakeholder relationships deteriorate, and reputational damage follows. In this context, trust is not a byproduct of compliance. It is increasingly treated as a core regulatory outcome.

The Hidden Risk of Reactive Compliance

Reactive compliance models are becoming increasingly misaligned with current regulatory expectations. Approaches that focus on post-hoc remediation, incremental controls applied to legacy processes, or reliance on third-party assurances introduce structural weaknesses that delay risk identification, obscure accountability for AI-enabled outcomes, and limit the availability of timely, decision level audit evidence. 

In an AI-driven environment, automated decision making amplifies these weaknesses by operating at scale and speed, increasing both the impact of control failures and the difficulty of timely oversight. This shift has heightened supervisory scrutiny, with reactive compliance increasingly interpreted as a governance failure rather than a lapse in execution.

Moving from Policy Frameworks to Platform-Based Governance

To meet evolving regulatory expectations, compliance functions have an opportunity to progress from reactive, document-centric approaches to proactive, system-enabled governance models. This evolution enables governance to be embedded directly into the design and ongoing operation of AI-enabled processes across the client lifecycle, strengthening oversight, control, and regulatory confidence.

In practice, this entails the implementation of lifecycle-based governance, whereby AI systems are subject to oversight from initial design through deployment, monitoring, change management and eventual retirement, rather than being assessed solely at the point of implementation. Institutions must ensure explainability by design, enabling transparent and proportionate explanation of automated outcomes to support regulatory review, auditability, and internal governance.

Human oversight and control remain essential. Automation should enhance operational efficiency without displacing accountability and oversight, and governance frameworks must provide for human intervention, escalation, and override mechanisms within AI-driven workflows. In addition, auditability and traceability are critical, requiring that all AI-driven actions be comprehensively logged, traceable, and capable of reconstruction to support both internal risk management and external supervisory examination.

Finally, robust data protection and information security controls governing data usage, residency, access and isolation are foundational requirements in regulated AI environments. These considerations constitute operational requirements that regulators increasingly expect financial institutions to demonstrate in practice.

Proactive Compliance as a Strategic Advantage

While the regulatory drivers are clear, the implications extend beyond risk mitigation. Institutions that embed the compliance trinity of regulation, artificial intelligence, and trust into their operating model can generate measurable business value, including stronger supervisory outcomes, increased operational resilience, and enhanced confidence in AI-enabled decision making. In practice, this operating model also enables:

• Faster and more constructive regulatory engagement
• Greater confidence in scaling automation across high-risk functions
• Reduced remediation and supervisory costs over time
• Stronger client and stakeholder trust

Importantly, proactive compliance enables innovation rather than constraining it. By establishing clear governance boundaries and controls upfront, institutions can deploy AI capabilities with confidence, knowing that regulatory expectations are addressed at the outset by design.

Implications for Compliance and Risk Leadership

The regulatory environment facing financial institutions is increasingly defined by expectations of governance maturity rather than by the introduction of discrete regulatory requirements alone. As regulatory oversight becomes more continuous, artificial intelligence is more central to regulated activity, and trust is more directly assessable by supervisors, reactive compliance models present an increasing source of regulatory and operational risk.

Compliance and risk leaders should therefore consider the need to evolve compliance functions from reactive response mechanisms into proactive, system-enabled disciplines that integrate regulatory requirements, technology governance, and trust as core operational principles. Institutions that undertake this transition will be better positioned to meet supervisory expectations and to support sustainable and responsible innovation within regulated financial services.

Fenergo’s Approach to Trust and Regulatory Confidence

Fenergo’s approach is designed to meet regulatory expectations of trust by embedding governance, transparency, and control directly into its platform, enabling financial institutions to demonstrate accountability, auditability, and regulatory confidence in the operation of AI-enabled compliance processes. 

As outlined in Fenergo’s AI Governance whitepaper, governance is embedded through the SaaS AI Control Framework comprising over 30 discrete controls. These controls are mapped to relevant regulatory requirements and supervisory expectations and aligned with global best practices, supporting consistent and defensible oversight across regulatory regimes.

Rather than treating governance as an external overlay, Fenergo applies these 
controls across the full AI lifecycle, from design and development through deployment, monitoring and retirement, ensuring that trust is continuously maintained, not retrospectively justified.

To read more on Fenergo’s approach: read our AI Governance whitepaper.