Pattern recognition in finance

Lisa Haydon, founder and CEO of Halifax-based Pivotal Growth, has witnessed how AI transforms pattern recognition capabilities across organizations, including financial operations.

“The way that AI allows us to get at patterns and correlations is in the people performance side,” Haydon says. “In the past, when you looked at doing either consulting or using assessments, you were constrained by the consultants’ capacity to handle Excel-based spreadsheets, especially if they were qualitative agencies.”

But sophisticated AI tools enable a more comprehensive analysis. 

“We are now able to use both quantitative and qualitative data in the way we create insights for an organization, and so that allows some patterning and some much more robustness,” Haydon says.

This capability extends beyond HR applications to financial operations, where AI can identify trends and correlations that might signal risks or opportunities, she said. 

“As a consultant, what we’re able to do is really get focused on that gap assessment on a future focus basis with much more clarity. We are able to get at patterns far clearer, and that means our clients get a much more prioritized and data-backed point of view.”

Haydon adds AI has transformed decision-making, moving it from opinion-based to more evidence-based. 

“It was completely opinion based. And there’s still many organizations that make their leadership development investments based on opinion,” she says. “We’re actually taking this — what we have from data, insights, patterns, correlations — and then customizing it for an organization and being able to give them very specific insight.”

Detecting and addressing financial irregularities

When AI systems flag potential irregularities in financial data, a structured approach to human review becomes essential.

“Flagged items should be directed to human expert reviewers with sufficient contextual data from the AI,” Cristina Goldt, general manager of workforce and pay at Workday, says . “These reviewers can then analyze the information to distinguish between false positives and genuine problems, identifying root causes and determining necessary actions.”

The learning should flow both ways. “Importantly, what the human team learns should help the AI get better at spotting things correctly in the future,” she adds. “Keeping good records of all this helps keep things on track and helps us make the whole system even smarter over time.”

Develin agrees and recommends regular audits be part of this process. 

“Workflows and audits, right? So ensuring that the right eyes are on the information, and then periodically auditing what your AI tools are putting out there to ensure fairness and balance and that everything’s working properly.”