By the financeSHOWCASE Editorial Team
Featuring insights from Matthew Goldstraw, RSM
It is no secret that finance functions are under immense pressure to innovate. From automated invoice processing and predictive cash flow modeling to AI-assisted month-end closes, artificial intelligence promises unprecedented operational efficiency.
However, as the speed of adoption accelerates, so too does the risk.
At a recent financeSHOWCASE event, Matthew Goldstraw from RSM took our 5 in Twenty stage to deliver a vital reality check: “The Perils of AI in Finance.”
Matthew’s core message was clear: while AI can transform the back office, finance leaders must safeguard the business by striking a delicate balance – driving rapid innovation while maintaining strict risk controls, uncompromised data integrity, and flawless auditability.
Here is a breakdown of the key perils finance directors need to manage as AI becomes deeply embedded in corporate finance.
1. The ‘Black Box’ problem and loss of auditability
The primary duty of any Finance Director or Controller is to ensure that numbers are accurate, verifiable, and compliant. Traditional accounting systems leave a clear, linear audit trail: transaction A leads to journal entry B, approved by user C.
When AI algorithms enter the workflow – whether predicting bad debt provisions or classifying complex expenses – they often operate as a ‘black box.’
“If an AI model generates an output that directly impacts your financial statements, can you explain to an external auditor exactly how the system arrived at that number?”
If the answer is no, your business faces significant compliance and audit risk. External auditors and regulators will not accept “the algorithm decided” as a valid defense. Maintaining explainability and documenting human oversight at every stage of AI-assisted reporting is non-negotiable.
2. Garbage in, disastrous decisions out
AI algorithms do not possess inherent business sense; they learn strictly from the historical data fed into them. If your underlying financial data is fragmented, duplicate-heavy, or unstandardized, AI will not fix it – it will simply automate and accelerate bad decision-making.
- Data Lineage: Do you know where the data feeding your AI models originated?
- Algorithmic Bias: If an AI model bases cash flow forecasts on biased historical data, it can lead to severely flawed working capital strategies.
Before throwing AI at a process, finance teams must ensure their core data architecture and governance frameworks are watertight.
3. The new wave of AI-driven fraud
While finance teams use AI to optimize internal workflows, cybercriminals are using the exact same technology to exploit weaknesses.
The rise of generative AI has ushered in a sophisticated new era of financial fraud:
- Hyper-Realistic Phishing: AI can scrape public executive profiles to generate flawless, context-aware payment requests that easily bypass traditional spam filters.
- Deepfake Verification Bypasses: Voice cloning and video deepfakes are increasingly being deployed to trick junior finance staff into overriding standard dual-authorization payment controls.
Protecting the business requires updating internal verification protocols. Human confirmation steps—especially for changes to supplier bank details or high-value treasury transfers—must remain mandatory, regardless of how “urgent” or “authentic” an incoming digital request appears.
4. Over-reliance vs. Human judgment
One of the subtle perils Matthew highlighted during his session is cognitive offloading—the tendency for finance staff to trust software outputs without questioning them.
When automated reconciliation or anomaly detection tools boast 95% accuracy, employees can easily slip into autopilot mode, rubber-stamping outputs without critical analysis. The danger lies in the 5% where the AI gets it wrong.
AI should be treated as an assistant, not an autonomous decision-maker. The role of the modern finance team is shifting from data entry to data review, verification, and strategic challenge.
How finance leaders can protect the line
Innovation and risk management are not mutually exclusive—provided you build the right guardrails from day one:
- Establish Clear AI Governance: Implement a strict corporate policy on which AI tools are approved for use and how sensitive financial data can be inputted.
- Keep the Human in the Loop: Ensure mandatory manual sign-offs remain embedded in critical financial processes, particularly around financial reporting and cash releases.
- Audit Your Data First: Clean up legacy data and establish robust data lineage before deploying automated insight tools.
- Train Staff on AI Threats: Educate teams on deepfake tactics, social engineering, and the dangers of over-relying on automated outputs.
Balance speed with safeguards
AI offers finance functions unprecedented power to automate the mundane and elevate strategic insight. However, finance leaders who rush to adopt these tools without addressing data quality, auditability, and governance are taking a massive gamble.
By balancing rapid innovation with proactive risk management, CFOs can harness the power of AI while keeping their organizations completely protected.
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