Intelligent Oversight > How CFOs Can Use AI Agents to Better Manage & Assure Risk

In an environment defined by volatility, complexity, and heightened stakeholder scrutiny, risk oversight is no longer limited to managing potential threats. Chief Financial Officers must also provide – and seek from others – assurance that risks are being identified, assessed, and controlled systematically and in near real time. Leading Chief Risk Officer, Tom McLeod examines further for CFO Magazine A/NZ.

Artificial Intelligence, and in particular AI agents, offers finance leaders the opportunity to lead the transformation of both risk management and risk assurance functions. These technologies allow organisations to detect and respond to risks earlier, monitor control effectiveness continuously, and provide real-time visibility to boards, regulators, and auditors.

Reframing Risk Oversight

Traditional approaches to risk management and assurance rely on retrospective data, sample testing, and manual controls monitoring. These methods are not suited to fast-moving risks such as cyber threats, regulatory changes, or ESG-related events, which demand more responsive and data-driven oversight.

AI agents, being autonomous systems that observe data, learn from patterns, and act on predefined objectives, offer a path toward continuous monitoring and assurance. These tools can not only detect anomalies and emerging threats, but also verify whether controls are working as intended, generate alerts for governance teams, and maintain detailed audit trails of decision-making processes.

Applications of AI Agents in Risk Management and Assurance

Continuous Financial Risk Monitoring and Assurance

AI agents can be embedded within financial systems to monitor key metrics such as liquidity, credit exposure, revenue recognition, and cost anomalies. Beyond detection, they can verify compliance with financial policies and control thresholds in real time.

For assurance purposes, these agents can generate evidence logs and control testing results that feed directly into internal audit programs or external audit support, reducing the reliance on manual sampling.

Example: An AI agent may continuously monitor revenue transactions to ensure alignment with recognition principles, flagging exceptions for review and maintaining an audit trail for assurance reporting.

Third-Party Risk Oversight with Embedded Assurance

Third-party and supply chain risks require both proactive management and transparent oversight. AI agents can track supplier performance, scan for compliance breaches, and alert the organisation to geopolitical or other risks in external entities.

They also enable assurance by documenting due diligence steps, monitoring adherence to contractual obligations, and verifying compliance with procurement policies — thereby supporting reporting to regulators, boards, or rating agencies.

Example: An AI agent may monitor procurement activity against approved supplier lists, alerting finance and compliance teams to policy deviations and creating an assurance record of actions taken.

Automated Control Testing and Assurance Reporting

AI agents can significantly improve the efficiency and effectiveness of internal controls monitoring. By continuously analysing transactions, approvals, and system access, these agents can test control performance automatically and generate exceptions for further review.

This enables CFOs to provide real-time assurance over financial controls, reduce the audit burden, and respond faster to compliance issues.

Example: An AI agent reviewing general ledger entries can automatically identify unusual journal patterns, test them against segregation-of-duty rules, and escalate unresolved items — supporting both management action and audit documentation.

Integrating AI Agents into Governance Frameworks

To maximise the value of AI agents in risk assurance, CFOs should integrate these technologies into their existing governance structures.

This includes:

• Defining accountability for AI-generated insights and decisions
• Aligning AI outputs with internal audit plans and compliance metrics
• Ensuring data governance and model transparency for regulatory assurance

It is essential to treat AI agents not as isolated tools, but as digital extensions of internal control systems — contributing directly to risk oversight, audit readiness, and governance reporting.

Three Key Actions for CFOs

Deploy AI Agents in a Targeted, High-Risk Assurance Area

Select a core risk process — such as financial controls, vendor payments, or ESG compliance — where AI agents can deliver measurable value in both management and assurance. Begin with a limited scope, validate outputs, and expand based on results.

Embed AI Outputs into Internal Audit and Risk Reporting

Ensure that insights from AI agents are integrated into assurance dashboards, audit committee packs, and compliance reports. Use them to support assertions on control effectiveness and policy adherence.

Build Assurance Capabilities Around AI Systems

Equip finance and risk teams with the skills to interpret and verify AI-generated findings. Establish oversight protocols to review AI decisions and maintain accountability, ensuring assurance remains both robust and transparent.

The evolving risk landscape requires CFOs to take a more dynamic and continuous approach to both managing and assuring risk.

AI agents provide the necessary infrastructure to monitor, test, and validate risks and controls in real time — enhancing visibility, reducing lag, and improving confidence in the organisation’s risk posture.

By embedding AI agents into risk and assurance frameworks, CFOs can deliver stronger governance, faster response capabilities, and clearer accountability — all essential for today’s complex business environment.