30 December 2025 | Tuesday | News
Picture Courtesy | Public Domain
According to a recent survey from US-based Dragonfly Financial Technologies, more than 50% of banking executives expressed concern about their continued reliance on legacy technology. And for good reason: These systems were built for a different era, before the ever-more-complex regulatory environment and expectations around customer remediation. To address this, Jayesh Kumar, Co-Founder at Aonami, joins host Karla Jo Helms on the Disruption Interruption podcast to discuss how he is replacing entire operation stacks with unified AI solutions. "True disruption really happens not by doing a lot of work, but by doing less in a much smarter way. You should be building systems that start giving time back to people," says Kumar.
Modern finance is facing a crisis of efficiency. While banks aim to provide seamless user experiences, their backend reality is often a patchwork of outdated systems that stifle innovation. This fragmentation creates a security nightmare where crucial functions are slow, expensive, and vulnerable to errors.
Kumar points out that the sheer volume of disparate systems makes agility nearly impossible. "If a financial institution wants to provide a seamless experience, a single change often needs to reflect across 15 different systems," explains Kumar. "In regulated industries, compliance sign-off for just one small change can take 10 days. By the time a groundbreaking product goes to market, it's been a year and a half—meaning banks are constantly playing catch-up and firefighting rather than innovating."
This constant state of "firefighting" leads to employee burnout and operational apathy. "Most of the time is spent handling exceptions and managing compliance rather than focusing on the next layer of innovation," Kumar notes. "Even though the intent to improve user experience is there, the time required to make it happen within legacy infrastructure is simply too great."
Fintech Business Asia, a business of FinTech Business Review
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