Sunil Padiyar, Chief Technology Officer at Trintech – Interview Series

Sunil Padiyar is a seasoned Chief Technology Officer (CTO) with a robust track record in architecting cloud-based B2B SAAS solutions for fast-growing software enterprises. As the CTO of Corcentric, he spearheaded revenue growth of 25% YoY, focusing on the CFO office by streamlining accounts payable, automation, procurement, and payment transactions. Sunil also held the role of CTO at FacilitySource, overseeing its remarkable expansion from $40 million to $250 million in just 5 years, culminating in a successful acquisition by CBRE.

Trintech is a global provider of cloud-based software that automates financial close processes and account reconciliations for finance and accounting teams. With over 35 years of experience, it supports thousands of customers across more than 100 countries, processing billions of transactions annually. The company leverages AI-driven automation and strategic partnerships to help finance professionals shift from manual tasks to higher-value work.

You’ve spent over two decades in technology leadership roles—what first drew you toward building solutions that empower finance teams, and how has that motivation evolved over your career?

Early in my career, I saw how much time and energy finance teams spent on repetitive, manual work — and how little of their day was left for analysis and decision-making. It became clear that technology could shift that balance, letting finance professionals focus on higher-value activities. Over time, my motivation has evolved from simply automating tasks to reimagining workflows entirely, so that AI and automation aren’t just saving time, but also improving accuracy, compliance, and the overall quality of financial insights.

How is Trintech currently leveraging AI within its own internal operations to improve efficiency or decision-making?

Internally, we use AI to streamline processes across multiple teams. In operations, AI helps us identify and prioritize support tickets, enabling faster resolution for our customers. In product development, we use AI-assisted coding tools to accelerate feature delivery while maintaining quality standards. We’re also experimenting with AI for analyzing customer feedback at scale, helping our product and customer success teams spot trends and opportunities much faster than traditional methods.

Can you walk us through how AI is embedded in Trintech’s products to help finance teams close faster, reduce risk, or improve accuracy?

AI isn’t bolted onto our products — it’s embedded into their core workflows. In our Cadency platform, AI works in-context, with full awareness of the customer’s environment, data, and compliance requirements. This allows features like automated journal entry creation, exception prioritization, and dynamic reconciliation schedules to be tailored for each organization. In our Adra platform, AI is built into the user interface to deliver quick wins: recommending match rules, mapping data fields, and assisting with journal creation — all at the click of a button. This makes AI accessible and impactful for users without requiring complex setup.

Are there specific AI-driven features in Cadency or Adra that you feel have been game-changers for your customers?

For Cadency, Risk-Intelligent Automation has been transformative — automatically prioritizing and routing exceptions based on risk, and reducing manual review by up to 90%. In addition, our journal entry automation can cut the time to create a single entry from as many as nine minutes down to about one minute. Across hundreds of entries, the efficiency gains for finance teams are enormous.

For Adra, AI is improving productivity at every stage of the close. Features like MatchAssist recommend transaction matching rules, significantly reducing the manual effort to configure matching logic. AI also formats incoming data and auto-populates fields during import, eliminating repetitive work and improving match rates — all of which speeds up reconciliation and reduces user fatigue.

How does your team decide which AI capabilities to develop next—what’s the process from idea to implementation?

We start with the customer’s pain points. Our product managers, customer success teams, and AI specialists work together to identify where AI can make the biggest impact. From there, we prioritize opportunities based on value, feasibility, and alignment with our product vision. Prototypes are developed quickly and tested with a small set of customers to validate impact. Only after proving measurable results do we roll them out more broadly.

What role does AI play in detecting anomalies, preventing errors, or enhancing compliance in financial workflows?

Our AI analyzes both the data entered by the user and the underlying transactional data for anomalies, potential errors, and compliance breaches. Because our AI is context-aware and has the intelligence to recognize the specific customer it’s serving, it can enforce compliance rules that are tailored to that customer’s governance and regulatory framework. This means the system isn’t just generically flagging issues — it’s applying the right checks for that organization, ensuring accuracy and compliance without slowing the process.

How do you ensure that AI outputs in your solutions are explainable and trusted by finance teams who depend on them?

We design AI outputs to be fully transparent. Every recommendation is accompanied by the reasoning or data pattern behind it, so users can understand why the system made that suggestion. In Cadency, for example, exception risk scores show the factors that contributed to the score, allowing finance teams to validate the logic before acting. This transparency builds trust and ensures that AI remains an assistant, not a black box.

Can you share a success story where a customer achieved measurable results because of AI-powered automation in your platform?

One of our enterprise customers is currently piloting our AI-powered journal entry automation in Cadency. Prior to AI, creating a single journal entry could take as long as nine minutes of manual work. With our automation, that same process now takes roughly one minute. Across thousands of journal entries processed each month, the expected efficiency gains are enormous — translating into significant cost savings and freeing up finance teams for more strategic work. The pilot is already delivering strong results and is expected to go live in the near future.

How does Trintech balance AI innovation with maintaining the security and integrity of sensitive financial data?

Security is non-negotiable.

All our AI services operate in a stateless configuration, meaning customer data is not retained or used for model training. Proprietary ML models are hosted in private cloud environments, and generative AI is accessed through enterprise-grade APIs from Microsoft Azure’s OpenAI service. Data is encrypted in transit and at rest, and our systems are SOC 2 and ISO 27001 certified. This ensures that innovation never comes at the expense of data integrity.

Looking ahead, how do you envision AI transforming both Trintech’s internal operations and the way customers experience your products over the next five years?

Over the next five years, AI will shift from being a feature within our solutions to an orchestrator of the entire financial close process. For customers, this means AI will not only automate individual tasks but also anticipate what needs to happen next, orchestrate workflows across systems, and surface insights before issues arise. Internally, we’ll continue to use AI to improve efficiency in R&D, customer support, and operations, ensuring we can deliver more value to customers faster.

Thank you for the great interview, readers who wish to learn more should visit Trintech.