One of the toughest challenges in banking is balancing risk assessment with operational efficiency. Retail banking faces rising servicing costs due to high transaction volumes, while institutional banking demands precision in large-scale operations. Traditional methods often fall short in keeping up with the speed and complexity of modern banking needs. This is where AI led firms are transforming the industry. Dyna.Ai, a leading AI-as-a-Service company, provides cutting-edge AIdriven products and services that help businesses and financial institutions enhance system operations, improve customer acquisition and activation, optimize risk management, boost operational productivity, reduce costs, and ultimately build an efficient banking ecosystem.
Driving this transformation is Tomas Skoumal, CCo-Founder and Chairman of Dyna.Ai. With 24 years of experience in retail banking across eight countries, he witnessed the industry shift from a dynamic, innovation-driven space to one increasingly constrained by regulatory demands. Seeking a way to break free from these limitations, he transitioned to fintech, leading AI-driven advancements in risk and efficiency, among others. Under his leadership, Dyna. Ai is redefining how the finance industry leverage AI to enhance decision-making, automate processes, and improve customer experiences.
In the following conversation with CEO Insights Asia magazine, Tomas Skoumal discusses Dyna.Ai’s role in revolutionizing banking through AI-driven solutions, ensuring financial institutions stay ahead in a rapidly evolving digital landscape.
What led to your transition from banking to fintech?
With 24 years in banking, I gained knowledge and experience in the sector, but over time, the work became increasingly repetitive. Banking evolved into a highly regulated industry, shifting its focus from sales and innovation to compliance and risk management. While innovation continued, the implementation slowed down under stricter regulations.
Leaving banking felt like releasing the brakes. Now, leading a company that serves the financial sector, I have the freedom to think beyond regulatory constraints, explore transformative solutions, and drive real innovation. In fintech, innovation isn’t optional, it’s a must. That shift has been so refreshing and energizing for me.
AI-driven efficiency is shaping the future of banking, from automating routine tasks to optimizing customer engagement
What key challenges in finance stand out currently, and how does Dyna.Ai address them?
Institutional and retail banking operate differently. While institutional banking handles fewer but larger transactions, retail banking deals with a high volume of smaller transactions, increasing servicing costs and affecting efficiency. Implementing AI enhances efficiency in two key areas.
Discriminative AI, which includes machine learning and scorecards, refines risk assessment, fraud detection, and even marketing by predicting customer behaviour such as the likelihood of loan repayment or product purchases.
Generative AI, powered by large language models, automates repetitive tasks, allowing human employees to focus on more complex, high-value work. It also strengthens discriminative AI by analysing customer interactions across calls, texts, and chats, feeding valuable data into machine learning models. This feedback loop continuously refines risk assessment and customer profiling, enhancing decision-making.
At Dyna.Ai, we specialize in both areas, helping financial institutions streamline customer communication and leverage AI-driven insights for smarter, and more efficient banking.
How do you refine strategies with AI in risk management, sales, and beyond?
Regulatory constraints and data protection laws limit the data available for risk assessment. While traditional models relied on a few attributes, machine learning incorporates thousands, but regulatory limits shrink available data. Generative AI helps bridge this gap by creating alternative attributes, while improved credit bureau data further strengthens scorecards.
AI also enhances decision-making beyond risk management. It streamlines loan applications by reducing paperwork, making the process more accessible to low-risk borrowers. In sales, AI-driven insights personalize customer interactions, optimizing product recommendations and improving conversion rates. As risk assessment and sales strategies become more precise, banks can offer competitive pricing, attract quality borrowers, and create a cycle of reduced risk and better financial outcomes.
What new opportunities do you see in AI-driven financial innovation?
AI is transforming finance in three key areas. First, external AI agents such as voice bots, chatbots, and avatars are replacing human agents in customer service, reducing labour costs while improving efficiency. Second, internal AI agents are enhancing employee productivity through AI-driven training bots and analytical assistants, streamlining tasks like risk assessment and report generation. Third, advanced machine learning is refining risk assessment and customer profiling, enabling faster and more accurate decision-making.
While AI automates many processes, human oversight remains crucial, especially in regulatory compliance. At Dyna.Ai, we see AI as the future of customer-company interactions, offering cost-effective and scalable solutions.
How has innovation shaped your learning and approach at Dyna.Ai?
Every generation believes it is luckier than the previous one, and in some ways, that’s true. In the past, learning was mostly limited to reading, which was timeconsuming, especially after a long workday. But now, with podcasts and audiobooks, I can continue learning effortlessly.
Our R&D team, filled with young and curious minds, brings condense insights and fresh ideas to discussions. Their fresh perspectives lead to innovations I might not have considered otherwise.
For example, while our focus has traditionally been on B2B solutions for banks, the team recently pitched a new innovative product that could potentially shake things up in the B2C segment. This kind of thinking pushes the boundaries of innovation.
What leadership principles drive you, and what advice would you give to future industry leaders?
People perform best when they aren’t micromanaged but are given responsibilities and the freedom to voice their ideas. Leadership isn’t just about listening, it’s about understanding different perspectives shaped by unique experiences, culture, or roles.
In banking, for example, risk and sales teams both work toward profitability but through different approaches, one by minimizing losses and the other by driving revenue. When they recognize the value in each other’s viewpoints, they find stronger, more balanced solutions. The same principle applies to leadership. True innovation happens when diverse perspectives challenge each other.
However, fostering diverse perspectives isn’t enough if the mindset remains risk-averse. Too many industries allocate 70–80 percent of their time mitigating risks and only 20–30 percent exploring potential. This imbalance stifles progress. Instead, leaders should change the equation, prioritizing innovation while ensuring risk management remains a necessary but supportive function.
Tomas Skoumal, Co-Founder & Chairman, Dyna.Ai
With 24 years of retail banking experience across 8 countries, including roles at Citibank and Standard Chartered Bank, Tomas excels in risk management and sales. He held global board positions for 9 years, managing sales operations for 6 years before transitioning from a 17-year stint in risk roles.
• Hobbies: Skiing, golf, spending time with my kids
• Favorite Cuisine: Indian, Italian, Thai
• Favorite Book: The Last Convertible by Anton Myrer and works by Czech author
• Favorite Travel Destination: Val Thorens (France) and Cambodia