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Let’s be honest: every audit partner has asked the same question at least once during the year: “Is AI actually going to make our audits safer, or am I just inviting new risks into the practice?”
The short answer is yes, but only when firms use it deliberately, with strong governance, measurable KPIs, and the right human judgement factored in. For audit and accounting firm partners, the question isn’t whether AI can reduce audit risk, because we all know that it can. The real question is, how to capture the risk-reduction benefits while avoiding new, self-inflicted risks.
So, the answer to the question isn’t a dramatic yes or a fearful no. It’s more grounded: AI reduces audit risk when used properly, and increases it when used blindly.
Let’s unpack what integrating artificial intelligence in auditing “properly” really means in the day-to-day of running an audit practice.
Audit risk is the chance that financial statements contain a material misstatement that the auditor fails to detect. It comes from three sources:
Reducing detection risk is where data-driven tech, including AI, can make a real, measurable difference.
However, you also need skilled auditors to run this properly. That’s where QX can play a significant role. Our bespoke audit support services empower you with the right talent skilled in the latest tech and AI tools, making transition a smooth practice for your firm.
Let’s skip the buzzwords and talk practical impact.
AI can scan every single transaction and point you to the five that actually look odd.
This isn’t theory. Tools like EY’s anomaly detection have already been used to sift millions of GL entries and find “needles” auditors would never have sampled manually.
This changes the conversation from “Did we sample enough?” to “Did we investigate the right things?”
Continuous analytics can flag sudden spikes, duplicate payments, odd vendor patterns or round-number invoices way before your team is in year-end mode. This alone reduces nasty surprises that blow up your budgets.
Artificial intelligence in auditing reads contracts, invoices and emails faster than juniors ever will, meaning you can test entire populations of documents instead of dipping in and out. This helps avoid the “we didn’t see that clause” moments that every partner fears.
Think of AI outputs as giant highlighters. They point you to the areas that deserve real scepticism. You still make the call.
Partners who use AI well don’t audit less, they audit smarter.
When fraud happens, it’s rarely a single glamorous transaction. It’s a pattern.
AI picks up patterns. Humans pick up the story.
Together, you reduce risk.
Here are a few stats worth paying attention to:
Here’s the part most shiny webinars skip:
Here’s a practical, partner-friendly playbook:
Pick ONE area: revenue testing, AP anomalies, contract reviews, payroll.
Then define what success looks like.
Examples:
Run the model on past audits where you already know the issues.
If it doesn’t catch what it should, don’t deploy it.
AI surfaces items. Humans decide what’s real.
Use AI to detect anomalies and require a partner sign-off for escalations and final conclusions.
Model version, validation date, thresholds, false positive rates, limitations.
Keep notebooks (or equivalent) documenting training data, versions, validation tests and limitations in case you’re a compliance query comes up.
Partners need to understand false positives, drift, risk scoring and thresholds. Otherwise, the review chain becomes the new weak link.
Just like internal controls, AI tools need monitoring. Set alerts for changes in false positive rates and revalidate quarterly.
Be transparent about how AI is used and how it affects the audit evidence chain.
The ROI of artificial intelligence in auditing can be measured in three things:
AI doesn’t make audits cheaper.
It makes them cleaner and safer when done right.
A. No blanket ban. But regulators expect firms to demonstrate governance, validation and measurement. The FRC review emphasises the need for KPIs and oversight. The best practice is to document, validate, and report in time.
A. No. AI handles scale and pattern-finding; partners supply judgement, scepticism, and final assurance. Firms that treat AI as a replacement introduce more detection risk, not less.
A. Start with high-volume, structured processes, such as purchase ledger, payroll, revenue recognition contract extraction, or bank-transaction anomaly scoring. These provide clear inputs and measurable outcomes.
A. It varies by dataset and model quality. Academic and practitioner studies report meaningful improvements in detection rates and efficiency, but numbers depend heavily on data quality and governance. Use back-testing in your own client base to set realistic expectations.
A. They offer scale and integration, but scale isn’t a substitute for local governance. The FRC found even large firms often lack KPIs; your governance matters more than vendor brand.
AI isn’t a magic wand, and it’s not a threat to your staff.
It is a set of tools that, when used with discipline, can genuinely reduce detection risk and strengthen audit quality – something every partner cares about.
The firms that will benefit aren’t the ones buying the flashiest tools. They’re the ones asking the right questions:
If you get those three right, AI becomes an advantage, not a liability.
Sanket is an audit expert with over a decade of experience in statutory audits across the UK and Ireland. He has extensive expertise in managing the complete audit lifecycle, from planning to finalisation, and is adept at leading diverse teams and handling multiple clients across industries. Recognised for his strong technical acumen, effective stakeholder communication, and ability to deliver high-quality audits within tight deadlines, Sanket is trusted for his precision and professionalism.
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