The Risks of AI in Finance: What Every CFO Needs to Know

The Risks of AI in Finance: What Every CFO Needs to Know

AI is transforming the finance function, but it brings real risks that CFOs and finance leaders must understand and actively manage. This is not an argument against AI adoption — the productivity and quality benefits are genuine and significant. It is an argument for informed, governed adoption that understands the risks and builds appropriate controls.

Risk 1: Accuracy and Hallucination

The most widely discussed risk of AI tools is their tendency to produce confident, plausible-sounding outputs that are factually incorrect. This phenomenon — often called hallucination — is particularly dangerous in finance contexts because:

• Financial figures that are slightly wrong can have material consequences
• AI confidence does not correlate with accuracy — a wrong answer looks the same as a right one
• The errors are not random and obvious; they tend to be subtle and contextually plausible

The management approach: All AI-generated financial content must be reviewed and verified by a qualified professional before use. AI outputs should be treated like draft work from a junior colleague: useful, but not trusted without checking. Key figures should always be verified against source documents.

Risk 2: Data Security and Confidentiality

Finance functions handle sensitive data: management accounts, budgets, personal data, client financial information, and potentially price-sensitive information. Uploading this data to external AI tools without understanding the data handling implications creates several risks:

• Data may be retained and used to train future AI models
• Data may be accessible to AI provider employees in support contexts
• Data breaches at AI providers could expose finance function data
• Regulatory obligations (GDPR, sector-specific regulations) may be breached

The management approach: Implement a data classification policy that defines which data can be used with which AI tools. Enterprise versions of major AI tools typically have stronger data handling commitments than consumer tiers. Price-sensitive information and restricted data should not be entered into any external AI tool.

Risk 3: Professional Accountability Gaps

Finance professionals — particularly qualified accountants — have professional accountability for the work they produce. AI tools do not change this: an accountant who signs off AI-generated financial statements is accountable for those statements, regardless of how they were prepared.

The risk is that AI tools can create a false sense of rigour — outputs that look professionally produced but have not been reviewed with appropriate scepticism. This risk is heightened when:

• AI tools are used under time pressure without adequate review
• Staff are not trained to evaluate AI outputs critically
• Governance frameworks do not require documented review of AI-assisted outputs

The management approach: Establish clear output review requirements for AI-assisted content, particularly for outputs that appear in financial statements, regulatory filings, client reports, or audit workpapers. Document the review process.

Risk 4: Over-Reliance and Skills Atrophy

A less-discussed risk of AI adoption is the potential for over-reliance: if finance professionals use AI tools as a substitute for developing their own analytical skills, the function may become dependent on tools that could become unavailable, inaccurate, or outdated.

This risk is particularly relevant for early-career finance professionals whose core analytical skills are still developing. AI tools used as a shortcut — rather than as a productivity multiplier for existing skills — can impede professional development.

The management approach: Position AI tools as productivity multipliers that amplify existing skills, not replacements for developing those skills. Early-career finance professionals should develop strong foundational skills before relying heavily on AI assistance.

Risk 5: Model Bias and Systematic Errors

AI models can reflect biases present in their training data, and they can make systematic errors that are consistent across similar inputs. In finance contexts, this means:

• AI tools may produce systematically biased analysis of certain types of companies or markets
• AI tools trained on historical data may not reflect current market conditions or regulatory changes
• Errors that are consistent and plausible are harder to detect than random errors

The management approach: Treat AI outputs with professional scepticism and validate key outputs against multiple sources. Do not rely solely on AI analysis for significant financial decisions.

The Risk of Not Adopting AI

It is worth noting that there is also a risk in not adopting AI: competitive disadvantage. Finance functions that do not develop AI capabilities will increasingly face slower output timelines, higher unit costs per analysis, and reduced ability to attract and retain talent. The risk management challenge is not to avoid AI, but to adopt it with appropriate governance.

Related Reading

• AI Governance for the Finance Function: A Practical Guide
• How to Implement AI Responsibly in Your Finance Team
• AI Ethics in Finance: What Finance Professionals Need to Know

---

Learnsignal's AI for Finance Professionals programme includes dedicated modules on AI risk and governance for finance professionals. Join the waitlist.