© All rights reserved. Powered by Techronicler
In Conversation - Pavlo Martinovych, Senior Product Manager, Uptiq
Stanley Anto, Chief Editor of Techronicler, in conversation with Pavlo Martinovych, Senior Product Manager at Uptiq
The global AI in finance market is rapidly expanding – from an estimated $38 billion in 2024 to nearly $190 billion by 2030, representing a compound annual growth rate of approximately 30% according to MarketsandMarkets. However, achieving this growth in highly regulated environments remains uneven, as institutions grapple with integration complexity, data privacy concerns, and model explainability.
At the nexus of practical AI innovation and banking operations is Pavlo Martinovych, Senior Product Manager at UPTIQ – an enterprise-ready AI platform trusted by over 140 financial institutions and processing more than $1 billion in transactions. With eight years of experience in AI, automation, and data analytics, Pavlo has architected real-world AI workflows that dramatically cut loan processing from weeks to hours – without sacrificing compliance or transparency.
Our chief editor sat down with Pavlo to explore the challenges and opportunities for AI in financial services, his lessons from over 100 automation projects, and the data-driven future of SMB Banking and Lending industries.
Stanley Anto:
Pavlo, many people hear “AI in financial services” and think of futuristic banking robots or flashy consumer apps. What does it actually look like on the ground today?
Pavlo Martinovych:
The reality is far more operational than most imagine – and that’s a good thing. In financial services, AI shines in bounded, high-impact tasks: parsing and validating loan documents, spreading complex financials into structured formats, monitoring compliance requirements, or generating credit memos. It’s less about replacing bankers and more about augmenting them with precise, repeatable steps.
Take one of our clients – a leading commercial bank in California. Before deploying our AI agents, processing a loan package could take weeks. With our Document Collection, Spreading, and Credit Memo agents, they cut document review time by 47%, financial extraction time by 36%, and memo preparation by 63%. That’s the kind of transformation AI brings when it’s grounded in real operational workflows. Of course, even with results like these, the path to adoption isn’t straightforward.
Stanley Anto:
That sounds impressive, but why aren’t all financial institutions already doing this? What’s holding AI adoption back?
Pavlo Martinovych:
The biggest blocker is that AI gets over-pitched and under-integrated. Too often, people sell “AI will replace X” instead of “AI will remove Y hours of manual work.” Middle managers get nervous, security teams block deployments, and pilots fail because they’re bolted on instead of built into existing systems.
There’s also a trust gap. In lending, 95% accuracy sounds good – until you realize one misread figure can derail an entire underwriting process. That’s why human-in-the-loop workflows, explainability, and compliance-grade privacy are critical. We use privately deployed models, link every output back to the original data field, and keep sensitive data in the client’s cloud. Without that foundation, AI doesn’t stick.
Stanley Anto:
For leaders deciding whether to bring AI into a process, what’s the first filter you apply?
Pavlo Martinovych:
I look at three things: productivity gain (does it free at least 0.2 FTE per month?), time ROI (will it save at least three times the build time annually?), and financial ROI (a 300–500% annual return is my benchmark).
You also have to ask if the process is actually understood. AI can’t fix unclear or outdated workflows – it will just automate bad SOPs faster. It’s less about “where can we put AI?” and more about “where will AI solve a known pain, with a clear before-and-after metric?” That framing makes adoption far more successful.
Stanley Anto:
AI agents have been one of the hottest topics in financial technology recently, but their role in finance isn’t always well understood. How would you explain the difference between AI agents and the automation banks have been using for years?
Pavlo Martinovych:
Traditional automation in financial services is usually hard-coded – you build a workflow for a specific process, and it follows that script forever unless a developer goes in and changes it. It’s like building a dedicated conveyor belt: great for one task, but rigid if you need to handle something new.
AI agents are different because they operate within a set of policies and contextual rules that can be updated behind the scenes. If the lending policy changes, the compliance thresholds shift, or a new product is introduced, you don’t have to rebuild the whole automation. The agent can adapt to those changes and learn the new way of execution over time.
This adaptability is critical in finance, where regulations, customer expectations, and even risk models evolve constantly. Agents don’t just execute tasks; they interpret the “why” behind the steps, recognize when the rules have changed, and adjust their actions accordingly. That’s the leap from static automation to living, policy-aware intelligence. And when you apply that adaptability to client-facing scenarios, that’s where we see a whole new dimension of value emerging.
Stanley Anto:
A lot of people still think of AI in banking as something that quietly works behind the scenes – processing data, detecting fraud, automating paperwork. Do you think AI has a role to play in customer-facing experiences?
Pavlo Martinovych:
Absolutely, and that’s where I think the next big leap will happen. For decades, banking relationships have been defined by either high touch or high tech – personal service on one side, digital convenience on the other. The real opportunity is to merge them.
We call this High Touch, High Tech. Imagine a relationship manager who can walk into a meeting already knowing the client’s latest financial trends, risks, and opportunities – all distilled from live, consent-based data. Or an SMB owner opening their banking app and receiving proactive, data-driven insights that feel tailor-made.
AI can make those moments possible by doing the heavy lifting in the background – connecting to authorized data sources, interpreting patterns, and surfacing relevant next steps – while the human banker focuses on empathy, trust, and strategy. This is especially true in SMB banking, where relationships drive loyalty and growth. Done right, AI doesn’t replace the relationship; it deepens it.
Stanley Anto:
Where do you see the fastest ROI for AI in financial services right now – is it in operational efficiency, customer engagement, or risk management?
Pavlo Martinovych:
In the short term, operational efficiency delivers the fastest measurable ROI – reducing time-to-decision, lowering manual review costs, and improving compliance accuracy. But in the medium term, customer engagement is where the long-term loyalty and revenue lift come from. That’s why I think the institutions that balance both will have a competitive edge.
Stanley Anto:
And looking ahead, where is this all going?
Pavlo Martinovych:
The next big frontier is data velocity. AI is only as good as the data it can train on and act upon – and in finance, that means having API-level access to live operational data. That’s why API integrations are becoming even more important than ever. These connections are always consent-based, meaning the customer explicitly authorizes access – a non-negotiable in today’s trust-driven financial landscape.
We’re integrating over 100 consent-based, owner-authorized data connectors to bring live SMB data – everything from accounting ledgers to e-commerce sales – directly into AI agents. For SMB banking, that means a relationship manager could see a client’s real-time revenue trend, spot early signs of cash flow stress, or identify when a business is expanding into new markets – and act on that insight immediately. Think of a secure conversational co-pilot for bank managers that already knows your policies and your customer’s complete financial picture. It can draft outreach, suggest the next product, or kick off a workflow – without a byte of data leaving the bank’s walls.
And it’s not just for bankers. Imagine a business owner opening their banking app and asking, “Can I afford to hire two more employees this quarter?” or “How will a 15% drop in online sales affect my cash flow next month?” Their personal AI assistant could run the analysis instantly, combining banking data, accounting records, and market conditions to give a tailored answer – not a generic FAQ.
Or take customer support: instead of routing a client through multiple departments, the assistant could instantly check loan covenants, verify payments, and suggest the best refinancing options – all in one secure conversation. This is the leap from “chatbots that read scripts” to digital advisors that understand your unique financial reality.
The real shift is that both financial institutions and customers will have always-available, context-aware AI partners – ones that continuously learn from live, authorized data and can act instantly, whether that means guiding a credit decision, preventing a cash flow crisis, or spotting the next growth opportunity before the numbers hit the monthly report.
Pavlo Martinovych combines deep financial expertise with product vision, helping companies make smarter financial decisions. With a foundation in mathematics, computer science, and economics, he began his career as a CPA-focused tax and business analyst. His transition into technology included serving as a Business Analyst at PNB Paribas Group, where he gained experience in large-scale financial systems modernization.
At Uptiq.ai, Pavlo leads the design of intelligent, embedded finance solutions for banks and fintechs. Previously, as Head of Product at upSWOT (acquired by Uptiq), he was instrumental in building platforms that integrated over 100 data sources to deliver real-time intelligence to financial institutions.
At the forefront of evolving analytics and AI integration, Pavlo’s work continues to shape how financial infrastructure evolves in the AI era, both in the U.S. and globally.