Global Fintech Interview with Mike Lynch, Principal, AI Strategy and Finance Transformation for Auditoria

Mike Lynch, Principal, AI Strategy and Finance Transformation for Auditoria chats about the current fintech innovations and trends dominating the global market in this interview:

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Hi Mike, take us through your fintech journey so far?

I really think my Fintech journey from a user standpoint started about 10 years ago when I started to see some of the new features and functionality that would appear within the ERP and other tools we were using at my company. It was great to see them be able to tie things together like doing personal mileage on an expense report with Google Maps. I also spent a number of years in financial regulation and so I also got to experience Fintech from how broker-dealers were innovating how to run a securities business and they were revolutionizing how their customers would access the securities markets. Of course, we also saw the rise of cryptocurrency in the financial markets and that continues to be a very disruptive force as people look for alternative investments for their money that aren’t necessarily tied to your traditional stocks, bonds, and other securities products.

What about today’s current state of fintech is most exciting?

I think when you look across the landscape of Fintech today, the most exciting thing is the rise of agentic AI in allowing digital coworkers to work side-by-side with your human employees to more quickly execute the mundane and menial tasks. This human-digital partnership frees the human to shift their focus to higher value and strategic work. I think as we look at the state of fintech over the next few years we’re going to continue to see a proliferation of AI agents in the workplace. I think a number of them will be commercially developed and delivered, but you are definitely going to see a rise of citizen developed bots in the ecosystem to allow them to automate the work that they do.

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Can you share a few thoughts on how the fintech market is set to shape up in a few years, what technology trends will dominate the space?

I think when we look at all of the impacts of AI in the next one to three years, it will likely be dominated by digital agents joining the workforce. I think the managers in the workforce today are the last managers who will have exclusively hired human beings into their departments. The workforce of the future will be a blend of human and digital workers. This shift in how we staff our finance departments is going to continue to have a long term ripple effect into a variety of areas, including: the speed at which we were able to derive insights from financial data, the speed at which we are able to close the books on a periodic basis, greater transparency and accountability into the AI that finance and other teams use, and we will start to see new types of jobs in the office of the CFO that are going to be rooted in financial expertise but we’ll also have flavors of data and analytics. It has been said by a number of analysts that a large percentage of the jobs that we will need about 3 years from now don’t even exist today.

Tell us about some of Auditoria’s AI innovations and how it enables end users in finance?

At Auditoria, we have our AI agents, we call them SmartBots, already working side by side with Finance team members, streamlining much of the repetitive low value tasks in the controllership, allowing them to work on strategic items for the office of the CFO. This has resulted in their ability to greatly improve the throughput of work and clear out backlogs of tasks. For example, in the account payable space teams use the SmartBots to ingest invoices, which not only speeds up the process but also has built-in capabilities to detect potential fraudulent invoicing.

We also deploy SmartBots in a helpdesk function to rapidly read through the copious amounts of email inquiries that AR and AP teams get, understanding the intent of what suppliers or customers are asking for, and being able to reply back to the original sender without making the human stop what they are doing and address those lower-value tasks. Our customers have been able to realize real dollar ROI savings, or they have been able to repurpose staff for strategic-level work. We are also eager to start deploying our SmartResearch SmartBot, which is going to deliver insights to the Finance teams, and allow them to conversationally query their financial data for scenario modeling and analysis. We launched this new product during the Workday Rising conference, and are eager to get it in the hands of Finance teams.

Five fintech takeaways for the near future you’d leave us with before we wrap up?

For 5 takeaways for Fintech, I would say the ones below, and we’re happy to help your teams separate the truth from the noise:

1. Start with your strategy – If your company’s goal this year is to reduce your period end close, ensure that anything you pursue from an AI standpoint furthers that goal.
2. Be outcome driven – With so many fintech tools out there, resist the urge to “implement something and see what benefits it gives us.” Be driven to change the metrics that are important, and determine what solution(s) will move the needle for your organization
3. Be open to change – Hiring these digital workers onto your team will mean that some of the older processes and procedures will need to change. Be open to exploring a little of the “art of the possible” with your teams, while still staying true to your values.
4. Don’t chase the “shiny penny” – There are new tools and services entering the market every day. Ensure those tools are built with responsible AI that is transparent and explainable. Look at the partnerships and pedigree of the companies you choose to invest in.
5. Be cautious with legacy solutions “Now with AI!” – While there have been some fantastic enhancements to legacy technologies (e.g., Copilot with Office365 for example), ensure you do your due diligence on tools that promise AI performance, with only new features. There are so many factors that go into being able to truly be an AI tool (e.g., data structure, selected language models, etc.) that you don’t want to put a shiny AI veneer on an old clunky software infrastructure, which will ultimately leave you wanting more.

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