Ensuring AI Governance in Finance: Mitigating Risks as Adoption Soars to 75 Percent

Apr 22

3 min read

Recent research from KPMG, reported by Consultancy.uk, reveals that 75% of finance teams now use AI tools, a sharp rise from 30% earlier in 2024. This rapid adoption reflects the growing confidence in AI’s ability to improve efficiency, accuracy, and insight in financial operations. Yet, only about two-fifths of these teams feel their assurance functions are fully prepared to manage the risks that come with AI.

Finance directors, accountancy leaders, and decision-makers in regulated SMEs face a critical challenge: how to keep governance, assurance, and review processes aligned with the pace of AI adoption. This post explores practical risks linked to AI use in finance and offers clear steps to strengthen governance, especially for firms using Microsoft 365, Copilot, or other AI-enabled tools.

Eye-level view of a computer screen displaying financial data with AI analytics dashboard

The Rapid Rise of AI in Finance Teams

The jump from 30% to 75% in AI adoption within months shows how quickly finance teams are embracing new technology. AI tools help automate routine tasks like data entry, forecasting, and report generation. They also support complex analysis, such as identifying anomalies or predicting cash flow trends.

This fast uptake is driven by the promise of time savings and improved decision-making. For example, Microsoft 365 users benefit from Copilot’s integration, which can draft financial reports or summarise data insights with minimal manual input. However, this speed creates a gap: governance and assurance processes often lag behind, leaving organisations exposed to risks.

Key Risks of AI Use in Finance

Finance teams must recognise several practical risks when using AI tools:

Inaccurate outputs: AI models depend on the quality of input data and algorithms. Poor data or flawed models can produce incorrect results, leading to wrong decisions or financial misstatements.
Poor data quality: AI tools require clean, consistent data. If data is incomplete, outdated, or inconsistent, AI outputs will be unreliable.
Unclear accountability: When AI generates reports or recommendations, it can be unclear who is responsible for verifying accuracy and making final decisions.
Weak permissions and access controls: Sensitive financial data must be protected. Without strict controls, AI tools could expose confidential information to unauthorised users.
Over-reliance on AI without review: Blind trust in AI outputs can cause errors to go unnoticed. Human oversight remains essential to validate results.
Exposure of sensitive finance data: Using cloud-based AI tools increases the risk of data breaches if security measures are insufficient.

These risks highlight the need for governance frameworks that keep pace with AI adoption.

Building Strong AI Governance Frameworks

Finance leaders can take practical steps to ensure AI use is safe, reliable, and compliant:

Develop Clear AI Policies

Establish written policies that define:

Which AI tools are approved for use
Types of data AI can access
Roles and responsibilities for AI oversight
Procedures for reviewing AI outputs before decisions

Clear policies set expectations and reduce ambiguity around AI use.

Implement Data Access Controls

Control who can access sensitive financial data within AI tools by:

Using role-based permissions
Regularly reviewing access rights
Encrypting data both in transit and at rest

This limits the risk of data leaks or misuse.

Provide Staff Training

Equip finance teams with training on:

How AI tools work and their limitations
Identifying potential errors in AI outputs
Following review and approval processes

Training builds confidence and accountability in AI use.

Document Review and Assurance Steps

Create standard procedures for:

Checking AI-generated reports against source data
Validating assumptions and calculations
Escalating discrepancies for further investigation

Documentation ensures consistent oversight and audit trails.

Conduct Regular AI Usage Audits

Schedule periodic reviews to:

Identify where AI tools are used across finance functions
Assess compliance with policies and controls
Update governance measures based on new risks or technology changes

Ongoing audits help maintain control as AI adoption evolves.

Practical Example: Using Microsoft 365 Copilot Safely

A mid-sized SME using Microsoft 365 Copilot for financial reporting might:

Restrict Copilot access to senior finance staff only
Require manual review of all AI-generated forecasts before board presentation
Train users on recognising when AI outputs seem inconsistent
Keep logs of AI interactions for audit purposes
Regularly update data sources feeding into Copilot to ensure accuracy

This approach balances the benefits of AI with necessary safeguards.

The Importance of Balanced AI Adoption

AI offers clear advantages for finance teams, but unchecked use can introduce risks that undermine trust and compliance. Governance is not about slowing innovation but about making AI adoption sustainable and safe.

By focusing on clear policies, strong data controls, staff training, documented reviews, and regular audits, finance leaders can harness AI’s power while protecting their organisations.

Finance directors and accountancy leaders should view AI governance as an ongoing priority. As AI tools evolve, so must assurance processes. This ensures finance teams remain confident in their outputs and compliant with regulatory expectations.