I’m thrilled to sit down with Nicholas Braiden, a trailblazer in the FinTech space and an early adopter of blockchain technology. With his deep expertise in financial technology, Nicholas has been a passionate advocate for its power to revolutionize digital payments and lending systems. Having advised numerous startups on harnessing tech for innovation, he brings a wealth of insight into how AI agents and robotic process automation are reshaping auditing and enterprise operations in the finance sector. Today, we’ll explore the transformative role of AI agents, their impact on moving from manual audits to comprehensive oversight, and how they complement human teams in driving efficiency and scale.
How would you describe the role of AI agents in the finance sector, and what sets them apart from traditional robotic process automation tools?
AI agents are a game-changer in finance because they go beyond the rigid, rule-based automation that robotic process automation, or RPA, offers. While RPA is fantastic for repetitive tasks like data entry or extracting specific fields from documents, AI agents bring a layer of intelligence and adaptability. They can analyze complex data, reason through discrepancies, and even suggest actions based on outcomes rather than just following a script. For instance, an AI agent might compare every line item in a contract against billing data and flag inconsistencies, whereas RPA would stop at pulling the data. It’s about moving from task automation to achieving broader business goals with insight.
In what ways are AI agents transforming the auditing process within finance, particularly compared to traditional methods?
They’re fundamentally changing audits by enabling what we call full-population assurance. Traditionally, audits relied on spot checks—sampling a small subset of data due to time and resource constraints. AI agents, however, can review every single transaction, invoice, or contract detail at scale. This shift means businesses get a complete picture of their financial health, not just a snapshot. It reduces risk, uncovers hidden issues, and boosts confidence in compliance. The ability to process massive datasets with precision is something manual processes or even RPA couldn’t dream of achieving at this level.
Why was it so challenging to analyze every invoice or contract detail against projections before the advent of AI agents?
The sheer volume and complexity of data made it nearly impossible. Manually reviewing every item in an enterprise resource planning system, for example, would take teams months, if not years, and still risk human error. Even with RPA, the focus was on automating specific steps, not synthesizing data or comparing it to forecasts. The limitations of manpower and technology meant businesses had to settle for sampling, which often missed critical outliers. AI agents overcome this by processing vast datasets quickly, applying logic to identify patterns or mismatches, and doing so without the fatigue or inconsistency humans face.
Can you elaborate on how AI agents support rather than replace human teams in financial operations?
Absolutely. AI agents are designed to augment human capabilities, not take over. In practice, this means they handle the heavy lifting—think reviewing thousands of contracts for price discrepancies or analyzing billing data against projections. This frees up human teams to focus on strategic decision-making, like interpreting the agent’s findings or approving recommended actions. Humans remain in the loop for judgment calls and nuanced decisions because trust and context are areas where people still excel. It’s a partnership where AI scales the workload, and humans provide the oversight and ethical grounding.
How do AI agents and RPA bots work together in a financial operation, and what unique strengths do they each bring to the table?
They’re complementary tools in a powerful workflow. RPA bots are the foundation—they tackle the predictable, rules-based tasks like extracting data from agreements or inputting figures into systems. Their consistency ensures a stable base. AI agents build on that by taking the extracted data and applying higher-level analysis, such as reconciling it against live billing or suggesting fixes for discrepancies. RPA is about efficiency in execution, while AI agents focus on intelligence and outcomes. Together, they create a seamless pipeline from raw data to actionable insights.
What are some common challenges or failures you’ve seen with bots, and how does their predictability help in resolving issues?
Bots often fail in predictable ways, which is actually a strength. For example, if a bot is programmed to pull data from a specific field in a document and the format changes, it’ll consistently fail to retrieve the right info. That uniformity makes troubleshooting straightforward—you can quickly pinpoint the issue, like a formatting mismatch, and adjust the script. This predictability is a stark contrast to more complex systems where errors might be sporadic. It’s like debugging a simple machine; once you fix the root cause, the bot runs smoothly again, providing a reliable foundation for more advanced tools like AI agents.
How do you envision the future relationship between AI agents and human teams evolving in the finance sector?
I see it becoming an even tighter collaboration. As AI agents grow more sophisticated, they’ll take on increasingly complex tasks, like proactively identifying trends or risks in financial data and proposing solutions before issues escalate. Imagine getting a notification from an agent saying, ‘There’s a discrepancy in this client’s billing, and here’s the recommended fix—shall I proceed?’ Humans will shift further into supervisory roles, focusing on strategy and ethics while trusting agents with analysis and execution. The key will be maintaining transparency and governance to ensure trust, but the synergy will drive unprecedented efficiency and insight.
What is your forecast for the role of AI agents in finance over the next decade?
I believe AI agents will become the backbone of financial operations within the next ten years. We’ll see them embedded in every aspect—from auditing and compliance to customer-facing services like personalized lending or payment solutions. Their ability to handle full-population data and deliver real-time insights will make manual processes obsolete in many areas. However, the human element will remain critical for oversight and innovation. My forecast is that the finance sector will evolve into a hybrid model where AI agents drive scale and precision, and humans steer the vision and values, creating a more resilient and responsive industry.