Garbage In, Confidence Out: How AI Makes Bad Financial Data Look Convincing
- Teresa Debevec
- 5 days ago
- 3 min read
The biggest risk with AI reporting is that it can make people feel more confident than they should. Imagine a committee or boardroom where a major investment decision rests solely on an AI-generated report. The report sounds polished and confident, but the numbers are flawed.
Parts 1 and 2 discussed how performance reporting starts with the balance sheet and why AI should be seen as a tool rather than a safety net. Building on this, we now examine the specific risks AI introduces to reporting.
In Part 3, we turn to 'Confidence Inflation Risk', the danger that AI can present flawed financial data convincingly, leading to costly mistakes if not carefully scrutinised.
This is because AI can sound convincing, even when misrepresenting the facts.
Garbage In, Confidence Out
'Garbage in, confidence out' illustrates how AI can mask errors and foster unwarranted trust.
Imagine an AI report showing a reconciled bank balance, but with other accounts unchecked. The narrative is clear, the logic is sound, and the conclusions seem reasonable. Because the report reads well and the numbers align with expectations, errors go unnoticed.
The mistake is missed, decisions are made, and risk grows.
Why Confidence Changes Behaviour
Clear, well-organised reports influence decisions. When a report is logical and confident, people tend to trust it. AI-generated reports often skip hesitation, caution, and scepticism, which directors and executives need to assess risks.
Misplaced confidence discourages key questions and weakens decision-making. People ask fewer questions and challenge assumptions less, reducing the healthy debate needed for sound decisions.
When Errors Become Harder to Detect
An executive asks, “Why did sales deviate from forecast?” This prompts closer review. Traditional reviews involve debate, questions, clarifications, uncertainty, and follow-ups.
Confident language in AI-generated reports can hide errors and inconsistencies.
A simple but important question should always be asked: “What if this is wrong?”
This mindset helps directors look past surface confidence and examine details. It supports the challenge needed for strong analysis.
The Professional Language Trap
Language matters more than most people realise. AI commentary is structured, logical, and persuasive.
This professional tone can make reports look complete, even if no one has checked the details. Unreconciled balances, missing liabilities, and cash flow risks may go unnoticed. These mistakes add up. When a report looks finished, people often assume it is correct.
Why AI Won’t Tell You Something Is Wrong
AI does not understand consequences. It describes what is there but does not ask why.
It can't assess if:
Results make business sense.
Trends are plausible.
A balance seems “off".
Numbers reflect operational reality.
Management, executives, and Boards still hold the responsibility and must remain accountable for ensuring data reliability.
How Can Governance Adapt
This prompts a key governance question: How must oversight change when AI blurs warning signs?
Verify the sources of financial data at every reporting stage to ensure their validity.
Confirm that all key financial processes are strictly followed to maintain data integrity.
Thoroughly review all assumptions underpinning reports to confirm their accuracy and relevance.
These steps strengthen fiduciary duty, ensuring AI doesn't undermine accuracy, transparency, or judgment.
How This Risk Quietly Escalates
When people trust AI-generated reports too quickly, mistakes can multiply unnoticed.
Decisions are made on flawed assumptions that build up over time. Fixes become more complex and disruptive, often surfacing only during audits, cash shortfalls, or compliance reviews. By the time these issues are found, they are usually serious.
Financial Discipline Still Matters
If used carefully, AI can provide valuable insights. If used too quickly, it can obscure risks. To manage AI-related risk effectively, follow these four key guidelines:
Ensure clean and complete data.
Perform consistent balance sheet reconciliations.
Require human review of every report.
Apply professional scepticism.
Always blend expert judgment with AI analysis
Anything less is just confidence without control.
Coming Next
Part 4 covers the challenge of embracing AI reporting without clear accountability.
Because insight without ownership isn't leadership; it creates risk.
