Patrice Bouexel explores how AI and automation are impacting employee expectations and behaviours in the wider financial ecosystem.
Now more than ever, we are seeing a landscape in which organisations globally are racing to automate their financial operations. For Patrice Bouexel, general manager for Europe at financial software platform Sis ID, this is evidence of an ecosystem in which companies are struggling to meet expectations.
Bouexel told SiliconRepublic.com, “Finance teams are under the same pressure as every other function – do more with the same headcount and do it faster.
“Gartner found that AI adoption in finance rose from 37pc in 2023 to 58pc in 2024, while Deloitte’s global survey of more than 1,300 finance leaders found that adopting new technology, including AI, was a top priority for 48pc of respondents.”
He added: “That is not a small shift. It reflects a recognition that manual, paper-based processes cannot keep pace with the volume and complexity of modern payment flows, especially as organisations operate across more currencies, more vendors and more jurisdictions than they did five years ago.”
Bouexel explained that for the vast majority of companies, automation is no longer a nice-to-have feature that boosts productivity – it is fast becoming a baseline expectation among boards and investors, who often view it as a central way a finance function manages not only risk, but cost as well.
Rough regulation
But it isn’t solely the introduction of new technologies creating opportunity and chaos for organisations. Among the more pressing challenges for modern-day finance professionals is ensuring that the data utilised and generated by advanced tech is of a high quality and appropriately governed.
“The gap sits between deploying a tool and trusting what it produces. Organisations that get this right treat automation as a governance exercise first,” said Bouexel.
“They map which processes are rules-based and genuinely ready for automation, such as invoice processing and expense approvals, and separate those from multi-stakeholder processes that need workflow redesign rather than a bot bolted onto an old process. They also invest in the underlying data before they invest in the algorithm.”
Skills also present a significant challenge, he finds, as AI, automation, data analysis and technology integration are becoming critical to experts in this field.
“Automation succeeds when finance teams are given the time and training to own the exception cases, not just the software licence.”
Sonic speed
He finds, too, that in an environment in which AI and automation are only ever as reliable as the financial data they are relying on, the hastiness to complete a task can often spell disaster.
Bouexel noted, “An automated process will execute a bad instruction just as efficiently as a good one. If a vendor record is outdated, if bank details were changed without proper verification or if a duplicate invoice slipped through months ago, automation does not catch that. It accelerates it.
“That is the uncomfortable part of the automation conversation that gets skipped over. Organisations are moving faster towards straight-through processing at exactly the point when the data feeding those processes – vendor master files, payment histories and approval trails – is often the least scrutinised part of the finance stack.
“Speed without verified data does not reduce risk. It just means an error or a fraudulent instruction reaches payment faster than it used to.”
He suggested that nowadays, the real goal is prevention rather than cleaning up a mess once it has already happened. As the detection of fraudulent or suspect behaviour becomes a more difficult challenge, largely due to AI and deepfake technologies, verification skills and processes can make all the difference.
“The more durable answer sits earlier in the chain, verifying who you are actually paying and confirming their identity through a channel the fraudster cannot touch before the payment is authorised, rather than trying to spot the anomaly once it is already in flight,” he said.
Laudable leadership
When asked the question, ‘What should finance leaders be doing now as fraud, AI and regulation reshape payment security?’, Bouexel remarked that there are three areas for leaders to focus on.
Firstly, they should separate process risk from technology risk.
Secondly, AI literacy must be built into finance teams as a means of developing a human-based tech skillset, not just into the tools being used.
He said, “Finance teams need to understand how AI-enabled fraud actually behaves if they are going to recognise and challenge convincing fraudulent requests.”
Lastly, they need to treat regulatory change as a floor, not a ceiling.
“Rules tend to formalise what good practice already looks like. Organisations that wait for the mandate before acting are, by definition, behind.”
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