Risk Management Strategies Take Centre Stage As Agentic AI Transforms Compliance

AI’s ‘wow’ moment has passed and AI-driven decision-making systems are increasingly embedded in core operations. But as AI tools gain ground, and agency, they also introduce a new class of risks that traditional compliance frameworks were never designed to manage. Agentic AI - systems that can flag suspicious transactions, recommend customer actions, or initiate alerts - offer speed and scale, but can also cause harm in milliseconds if left unchecked.

Nearly 70% of banking executives now say their AI deployments have outpaced internal risk controls, according to a recent report by BCG. When AI decisions can’t be explained, models degrade over time, or rogue outputs create regulatory violations, the consequences can be swift and severe.

For chief risk officers and compliance leaders, the challenge is clear: bring oversight back in line with innovation. This article explores how to adapt risk frameworks for AI models with human oversight and robust regulatory controls built in, to keep agentic AI in check without stifling its potential. As agentic AI gains traction, governance has become a critical strategic requirement.

Main Risks in the Age of Autonomous AI

Unlike traditional software programmes, which perform fixed tasks with predictable outcomes, agentic AI adapts in real time, learns from new data, and can operate with minimal human oversight. This autonomy brings powerful efficiencies but it may also lead to unintended risks.

• Unpredictable outputs can lead to operational errors that are difficult to trace. 

• Bias embedded in training data can drive systemic discrimination, particularly in areas like credit or fraud detection. 

• Meanwhile, always-on connectivity increases the surface area for cyberattacks, especially when models access sensitive customer data. 

• Transparency: many agentic AI decisions don’t include explanations of the rationale for the decision, making oversight and auditability challenging.

• Unsupervised customer-facing decisions. An AI agent might recommend account closures or alter customer communication without compliance review, potentially violating regulatory boundaries. 

The bottom line: financial institutions must recognise these threats early and embed robust controls before deployment – not after failure.

Regulatory and Ethical Expectations

Regulators worldwide are sending a clear message: AI innovation must be matched with rigorous oversight. In the EU, the AI Act classifies certain uses of AI in anti-money laundering (AML) as ‘high-risk’, requiring robust documentation, testing, and human oversight. Similarly, the Financial Action Task Force (FATF) emphasizes that AI adoption does not absolve institutions of their AML obligations – if an AI system fails to detect a suspicious transaction, the liability still rests with the bank.

Ethical principles are also becoming regulatory expectations. Banks must be able to explain how their AI models work, demonstrate that they treat customers equitably, and ensure that oversight structures are in place.

Foundations of a Strong AI Governance Framework

Robust AI governance frameworks start with embedding compliance at every stage of the life cycle of a model. Banks, and other regulated institutions, must implement model validation procedures tailored to AI, including stress testing and continuous performance monitoring. Equally critical are data quality checks and bias assessments to ensure fair and consistent outcomes across diverse populations.

Structurally, banks should establish dedicated AI governance committees to oversee use cases, monitor risk exposure, and enforce accountability. Existing model risk management (MRM) teams must also evolve – bringing in machine learning specialists who understand the nuances of modern AI systems. Just as credit risk boards are now the standard in banks, AI oversight bodies must now take on a comparable role.

Platforms like Silent Eight’s Iris 6 are purpose-built to meet these governance demands. Iris 6 provides continuous model validation, explainability, and robust audit trails that help institutions ensure their AI systems remain accurate, compliant, and accountable. With built-in capabilities for human-in-the-loop decision review and real-time performance monitoring, Iris 6 empowers compliance teams to stay in control, even at scale, across operations and jurisdictions. 

Humans in the Loop

For agentic AI to be safely integrated into financial operations, critical decisions must remain reviewable and overrideable by human operators. Humans must help train AI models, and continue to monitor their performance and improvement via continuous learning loops. 

On the technical side, safeguards should include rigorous sandbox testing before deployment, explainable AI to ensure model outputs are traceable and auditable, and comprehensive activity logs to ensure all AI-driven actions are recorded for review. 

Equally important is the organizational culture around AI. Compliance teams must be trained in AI ethics and risk, and encouraged to collaborate closely with data scientists. Regulators like Singapore’s MAS and the U.S. OCC now expect in-house AI literacy. Cultural alignment ensures AI is treated as a tool to manage, not a machine that can operate autonomously.

Risk Management and Agentic AI: a Strategic Requirement

As agentic AI becomes more deeply embedded in financial services, the line between innovation and exposure grows thinner. The speed and sophistication these systems offer can unlock real compliance value – but only when paired with robust governance. Without careful oversight, agentic AI has the potential to put at risk the very trust and stability upon which financial institutions are built.

For banks, AI must be treated as a regulated system requiring board-level attention, structured governance, and continuous scrutiny. Humans must continuously oversee the performance of AI systems and contribute to keeping them up to date. 

By proactively investing in oversight structures, training, and technical safeguards, banks are well positioned to lead the industry in responsible innovation.