Understanding AI-Driven Legacy Software Modernization in Finance

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I recently attended Re-Work’s AI in Finance Boston event and as an experienced product manager and engineer, what struck me as I listened is how difficult it is to maintain financial software products. In an industry built on calculating risk and managing assets, an unfortunate reality has crept in, slowing innovation to a snail’s pace and increasing risks across the board: technical debt.

I've seen this silent killer eat away at innovation, drain resources, and threaten the security of many companies, but in a regulated and quality controlled industry such as finance, where legacy software modernization is critical, those issues can manifest themselves tenfold. AI was promised as a magic fix, but the past few years have shown that when not used correctly, it creates more problems than it fixes.

‍

The Real Price Tag of Legacy Systems

‍

Let's be brutally honest: the numbers are staggering. Financial institutions have been pouring 55% of their IT budgets into maintaining legacy systems. That's not an investment – it's a bandaid on a wound that's getting deeper by the day. When your developers are spending 33% of their time on average wrestling with technical debt instead of building new features or improving customer experiences, you're not just losing money – you're losing your competitive edge.

‍

"In the Age of AI, Tech Debt is a Toxic Asset"

‍

This powerful statement resonates because it captures a fundamental truth: while AI and modern technology are revolutionizing finance, organizations anchored by legacy systems are watching from the sidelines. Your competitors aren't just building new features; they're building the future of finance.

‍

So what is tech debt?

‍

It starts out innocently: it’s the choices you make as you build, either by choosing an architecture which is good enough for now but won’t scale, or investing in a framework or set of patterns which your team and product eventually outgrows. It’s the thousands of errors that have built up over the years, the mysterious 200-line function that everyone's afraid to remove because "it might break something”, the quick fix that became permanent or that weird coding language that Ned made up in the 90s (Ned retired to Ecuador and no one can get hold of him…)

There are so many different types of tech debt, but essentially it’s these choices that accumulate over years to add up to a gnarly web of expensive and time consuming maintenance. But it’s not just the money and the time…

‍

Security Vulnerabilities

‍

In the world of legacy systems, security is an ever-present specter haunting your operations. These aging platforms were architected when today's sophisticated cyber threats were science fiction. Their fundamental security models weren't designed to handle modern attacks, transforming periodic updates into a high-stakes game of whack-a-mole that risks destabilizing interconnected systems.

‍

The Talent Exodus

‍

The human cost of maintaining legacy systems is perhaps the most painful aspect of technical debt. Young developers view legacy codebases as career dead ends, while senior engineers who understand complex systems are burning out from constant emergency calls and maintaining arcane processes that no one else comprehends.

‍

Innovation Paralysis

‍

The most insidious impact of technical debt to the business is what it prevents you from building. Legacy software modernization becomes critical as features that should take days stretch into months. While competitors roll out AI-powered capabilities, you're stuck in endless regression testing cycles. In the fast-moving fintech space, being six months late to market is an eternity.

Learn more about use cases in finance by checking out our work with Stash

‍

The AI Advantage: A New Path Forward for Legacy Systems

‍

While the challenges of technical debt may seem overwhelming, recent advances in artificial intelligence are offering a ray of hope for financial institutions looking to break free from their legacy constraints. Rather than accepting technical debt as an inevitable cost of doing business, forward-thinking organizations are leveraging AI-powered solutions to dramatically accelerate their modernization efforts – and the results are transformative.

Consider this real-world example of how AI can turn what seems like an insurmountable technical debt burden into a manageable, cost-effective modernization initiative.

‍

Multi-agent AI workflows can reduce costs by up to 90%

‍

At Stride, we have seen first hand how AI for software testing can help teams accelerate their delivery velocity by mitigating tech debt. One example: a $2B company was struggling to manage a codebase associated with an acquisition, which was built on the same core technology stack but suffered from a severe lack of test coverage, making it extremely difficult to integrate and evolve that code.

Stride measured initial test coverage at 21%, well below the company’s minimum goal of 70%. By using a proprietary multi-agent approach for software testing, Stride was able to raise coverage to 48% in 20 days; the internal development team estimated that same effort at 226 days – almost a full year’s effort for one developer. This cost reduction of more than 90% helped accelerate modernization efforts substantially, and showed the team that it was possible to pay down large amounts of technical debt without sacrificing forward progress.

The math is straightforward: you can continue spending 55% of your IT budget maintaining legacy systems, or invest in modernization that will:

‍

Making the Business Case for Change

‍

The financial services industry stands at a crossroads. Those who continue to kick the can down the road will find themselves increasingly unable to compete in a rapidly evolving market. The benefits are clear:

Reclaim the 33% of developer time currently spent on tech debt
Free up resources from the 55% of IT budget currently tied to legacy maintenance
Enable AI and machine learning integration
Attract and retain top talent

The good news? There's never been a better time to tackle this challenge. Modern tools, AI-assisted migration strategies, and proven methodologies can help you eliminate tech debt in finance more efficiently than ever before.

‍

Ready to start your legacy software modernization journey? Let's connect and discuss how we can help you transform your technical debt into technical equity.

---

Principal, AI @Stride

‍

Understanding AI-Driven Legacy Software Modernization in Finance

No items found.

I recently attended Re-Work’s AI in Finance Boston event and as an experienced product manager and engineer, what struck me as I listened is how difficult it is to maintain financial software products. In an industry built on calculating risk and managing assets, an unfortunate reality has crept in, slowing innovation to a snail’s pace and increasing risks across the board: technical debt.

I've seen this silent killer eat away at innovation, drain resources, and threaten the security of many companies, but in a regulated and quality controlled industry such as finance, where legacy software modernization is critical, those issues can manifest themselves tenfold. AI was promised as a magic fix, but the past few years have shown that when not used correctly, it creates more problems than it fixes.

‍

The Real Price Tag of Legacy Systems

‍

Let's be brutally honest: the numbers are staggering. Financial institutions have been pouring 55% of their IT budgets into maintaining legacy systems. That's not an investment – it's a bandaid on a wound that's getting deeper by the day. When your developers are spending 33% of their time on average wrestling with technical debt instead of building new features or improving customer experiences, you're not just losing money – you're losing your competitive edge.

‍

"In the Age of AI, Tech Debt is a Toxic Asset"

‍

This powerful statement resonates because it captures a fundamental truth: while AI and modern technology are revolutionizing finance, organizations anchored by legacy systems are watching from the sidelines. Your competitors aren't just building new features; they're building the future of finance.

‍

So what is tech debt?

‍

It starts out innocently: it’s the choices you make as you build, either by choosing an architecture which is good enough for now but won’t scale, or investing in a framework or set of patterns which your team and product eventually outgrows. It’s the thousands of errors that have built up over the years, the mysterious 200-line function that everyone's afraid to remove because "it might break something”, the quick fix that became permanent or that weird coding language that Ned made up in the 90s (Ned retired to Ecuador and no one can get hold of him…)

There are so many different types of tech debt, but essentially it’s these choices that accumulate over years to add up to a gnarly web of expensive and time consuming maintenance. But it’s not just the money and the time…

‍

Security Vulnerabilities

‍

In the world of legacy systems, security is an ever-present specter haunting your operations. These aging platforms were architected when today's sophisticated cyber threats were science fiction. Their fundamental security models weren't designed to handle modern attacks, transforming periodic updates into a high-stakes game of whack-a-mole that risks destabilizing interconnected systems.

‍

The Talent Exodus

‍

The human cost of maintaining legacy systems is perhaps the most painful aspect of technical debt. Young developers view legacy codebases as career dead ends, while senior engineers who understand complex systems are burning out from constant emergency calls and maintaining arcane processes that no one else comprehends.

‍

Innovation Paralysis

‍

The most insidious impact of technical debt to the business is what it prevents you from building. Legacy software modernization becomes critical as features that should take days stretch into months. While competitors roll out AI-powered capabilities, you're stuck in endless regression testing cycles. In the fast-moving fintech space, being six months late to market is an eternity.

Learn more about use cases in finance by checking out our work with Stash

‍

The AI Advantage: A New Path Forward for Legacy Systems

‍

While the challenges of technical debt may seem overwhelming, recent advances in artificial intelligence are offering a ray of hope for financial institutions looking to break free from their legacy constraints. Rather than accepting technical debt as an inevitable cost of doing business, forward-thinking organizations are leveraging AI-powered solutions to dramatically accelerate their modernization efforts – and the results are transformative.

Consider this real-world example of how AI can turn what seems like an insurmountable technical debt burden into a manageable, cost-effective modernization initiative.

‍

Multi-agent AI workflows can reduce costs by up to 90%

‍

At Stride, we have seen first hand how AI for software testing can help teams accelerate their delivery velocity by mitigating tech debt. One example: a $2B company was struggling to manage a codebase associated with an acquisition, which was built on the same core technology stack but suffered from a severe lack of test coverage, making it extremely difficult to integrate and evolve that code.

Stride measured initial test coverage at 21%, well below the company’s minimum goal of 70%. By using a proprietary multi-agent approach for software testing, Stride was able to raise coverage to 48% in 20 days; the internal development team estimated that same effort at 226 days – almost a full year’s effort for one developer. This cost reduction of more than 90% helped accelerate modernization efforts substantially, and showed the team that it was possible to pay down large amounts of technical debt without sacrificing forward progress.

The math is straightforward: you can continue spending 55% of your IT budget maintaining legacy systems, or invest in modernization that will:

‍

Making the Business Case for Change

‍

The financial services industry stands at a crossroads. Those who continue to kick the can down the road will find themselves increasingly unable to compete in a rapidly evolving market. The benefits are clear:

Reclaim the 33% of developer time currently spent on tech debt
Free up resources from the 55% of IT budget currently tied to legacy maintenance
Enable AI and machine learning integration
Attract and retain top talent

The good news? There's never been a better time to tackle this challenge. Modern tools, AI-assisted migration strategies, and proven methodologies can help you eliminate tech debt in finance more efficiently than ever before.

‍

Ready to start your legacy software modernization journey? Let's connect and discuss how we can help you transform your technical debt into technical equity.

---

Principal, AI @Stride

‍

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