Integrating AI Into Accounting: Banking Exchange Spotlight

An Interview with Rajeswaran Ayyadurai

To start, how did you get into accounting, auditing, and tax, and what led you to focus on integrating these areas with emerging technologies like AI?

I’ve always believed that accounting is the language of business, and what really drew me to this field is the ability to provide clear visibility into business performance.

My passion lies in transforming how businesses operate by designing reporting systems that provide the right insights at the right time. When management has clear, structured, and reliable information, they can make better decisions and focus more on strategy rather than just operations. Over time, that naturally led me to expand into auditing and tax as well, because they play a critical role in ensuring the reliability of financial data and aligning it with compliance and strategic objectives

What led me toward integrating these areas with AI was the gap I consistently saw between data and decision-making. A lot of time is spent on manual reconciliations, validations, and data issue resolution. Even with strong systems, the output still requires significant manual intervention, and by the time the information reaches management, it’s often delayed.

AI stood out because it can automate a lot of those repetitive tasks, identify patterns, and give insights in real time. So instead of spending time preparing data, we can focus on analyzing it and supporting decisions. So for me, AI is not just about automation—it’s about elevating the role of finance into a more strategic, forward-looking function.

You’ve worked across the US, Canada, and India with multinational clients. How have you seen financial operations evolve when accounting, auditing, and taxation are brought into a more unified system?

Earlier, accounting, auditing, and tax were almost sequential. You close the books, then the audit happens, and then tax adjustments follow. It was time-consuming and often involved a lot of back-and-forth.

As organizations started adopting more unified systems, especially with ERP platforms, that model began to change. Now, transactions are recorded with controls embedded from the beginning, audit trails are automatically generated, and tax considerations are factored in much earlier in the process.

The drivers vary a bit by region—for example, the US is very compliance-focused, while in India the push has been more toward digitization and scaling—but overall, organizations are moving toward the same goal: better visibility and less manual intervention.

So overall, the evolution has been from a reactive, compliance-driven model to a more proactive, insight-driven financial function.

From your experience with platforms like SAP FICO, NetSuite, QuickBooks, and Sage, where are you seeing the biggest gaps that AI is now helping to close?

Most of these systems are built around rules and those rules are very good at capturing transactions and presenting data in a structured way that works for general business needs. But the limitation is that they depend on predefined logic. So if something unusual happens, or if there’s a pattern that wasn’t anticipated, the system won’t necessarily catch it. Also, every business has its own nuances, and standard reports don’t always highlight the most important insights.

That’s where AI comes in. It looks beyond rules and analyzes patterns, identifies anomalies, and highlights trends that might otherwise go unnoticed. So the real value of AI is that it transforms structured data into actionable intelligence, which directly supports better business decisions.

How does integrating AI into accounting and auditing workflows improve data quality and strengthen internal controls in a practical, day-to-day sense?

With AI, validation happens in real time. As transactions are recorded, AI can compare them against historical patterns, expected behavior, and policy rules. If something looks unusual, whether it’s a duplicate entry, an incorrect classification, or an abnormal value, it gets flagged immediately. That improves data quality because you’re fixing issues at the source instead of later.

For example, if a transaction is posted outside normal patterns, say, an unusual vendor, amount, or timing, AI can flag it immediately, rather than it being discovered weeks later during reconciliation or audit.

From a controls perspective, it also changes how monitoring works. Instead of checking samples periodically, AI can continuously review all transactions and highlight only exceptions. So in day-to-day work, teams spend less time reviewing everything manually and more time focusing on the few items that actually need attention

What changes are you seeing in how organizations approach risk management and decision-making when they have access to faster, more connected financial insights?

Traditionally, organizations relied on historical data, things like month-end reports, audit findings, or periodic reviews, to identify risks. By the time an issue was identified, it had often already impacted the business.

With faster and more connected financial insights, that dynamic has changed. Organizations now have access to near real-time data across accounting, operations, and compliance functions which allows them to identify risks much earlier.

The biggest change I’m seeing is a shift from reactive risk management to proactive decision-making. Another important shift is in decision-making. Earlier, decisions were largely based on static reports and past performance. Now, with more integrated data and AI-driven insights, organizations are moving toward forward-looking analysis.

As companies look to modernize their financial systems, what are the most common mistakes they make when trying to bring together accounting, auditing, and AI, and how can they avoid them?

I think one of the biggest mistakes is trying to implement AI without first fixing the data. If the data isn’t clean or consistent, AI won’t give reliable results. Another common issue is treating AI as a separate tool instead of integrating it into existing workflows. That often creates more complexity rather than solving problems.

I also see companies jumping into AI without a clear use case. They know they want to use it, but they’re not sure exactly what problem they’re solving. And then there’s the people side; change management is often underestimated. If teams don’t trust or understand the system, adoption becomes a challenge.

To avoid these issues, I think it’s important to start with data quality, focus on specific problems, and take a phased approach rather than trying to transform everything at once.

Rajeswaran Ayyadurai is an accounting and tax professional with more than 17 years of experience across the United States, Canada, and India, specializing in financial auditing, taxation, and ERP systems. He has extensive expertise in managing end-to-end corporate and personal tax filings, including U.S. tax forms such as 1120, 1120S, 1065, and 1040, along with sales tax and payroll compliance for multinational clients.

Throughout his career, he has worked with a wide range of accounting and enterprise platforms, helping organizations improve financial accuracy, streamline reporting, and implement scalable systems. His experience spans internal and statutory audits, financial analysis, and project finance, supporting both private sector companies and public institutions.