How AI is Giving Old Legacy Systems a New Life

Let’s be honest: legacy systems can be a headache. Financial institutions rely on them, but over time, they can become more of a burden than a benefit. Think of all those COBOL-based systems that, while reliable, are now causing bottlenecks and making life difficult for financial institutions. Finding developers who can still work on them? Almost like hunting for unicorns!

But here’s the good news: what if there’s a way to modernize these old systems without the usual pain and frustration? At Critical, we’ve been working on a project that does just that—using AI to unlock the mysteries of legacy code and breathe new life into old tech. Let’s dive into how we’re doing it and why it can be a game changer.

Why Legacy Systems Are Holding Us Back

Many financial institutions are still running on code written decades ago. And while those systems are rock-solid (most times, anyway), they come with some serious technical challenges:

• Lack of experts: COBOL developers are becoming a rare breed, which means finding someone to maintain these systems can be tough.
• Difficult to modernize: Traditional methods to convert legacy code— COBOL to Java as one example—often leave you with messy, unmaintainable code and fail to address the underlying architecture.

At the end of the day, these hard-to-evolve systems can delay operations, increase costs, and reduce agility, impacting a company's ability to comply with regulations, meet market demands, and deliver a strong customer experience.

In a fast-paced industry, failing to modernize can mean falling behind.

But modernizing these systems doesn’t have to be a slow, frustrating process anymore. Let me show you how we did it.

Our AI-Powered Solution: Making Legacy Code Easier to Understand

So, what did we do? We developed an AI solution (we called it CoBot) that helps make sense of complex legacy systems.

But here’s the thing: we weren’t just aiming for code translation. The modernization patterns we observe are about more than just converting COBOL to Java (or any other language) and crossing our fingers. Applications are increasingly being rebuilt from scratch, with adaptations that make sense for modern client platforms and architecture patterns.

In this scenario, understanding can be more valuable than simple translation. CoBot dives into legacy code and explains how it works, breaking it down into easy-to-understand chunks so that developers—and even less technical roles—can make sense of it, preserve business logic, and smoothly migrate it to modern languages, architectures and platforms.

Here’s how our solution helps:

• Clear explanations: The AI dives into legacy programs and explains the functionality and business logic in a simple way.
• Pseudo-code generation: It creates pseudo-code, giving developers a solid starting point to rewrite the code in a modern language.
• Data insights: It helps teams understand how the legacy system’s data flows and how it interacts with databases.
• Code-flow diagrams: The solution generates diagrams to visualize how the code works, making it easier to follow.
• Real-time feedback: If you get stuck, the AI Bot (that’s why we called it CoBot) can answer questions about the code, helping you stay on track.

The Results: Speed, Efficiency, and Lower Costs

Here’s what happened when we tried this in a real-world scenario:

• Fast conversion: In just one week, just relying on our solution, we converted an application with 4,000 lines of COBOL into Java—a process that would normally either take much longer using traditional methods or require serious COBOL expertise.
• High success rate in testing: During testing, 80% of test cases passed on the first try, meaning less time spent on fixing bugs and more time moving forward.
• Deeper understanding of business logic: By preserving the business rules embedded in the code, we ensure that we don’t lose what’s most important—the logic that drives the business.

So, in a nutshell, by leveraging AI, we’ve made the modernization process smoother, faster, and cheaper. And that’s a win for everyone.

What Technology Did We Use?

Our solution is powered by some cutting edge Generative AI tech. Let’s dive into it:

• Large Language Models (LLMs): These models bring legacy code expertise into the equation. It’s the engine of the application.
• Retrieval-Augmented Generation (RAG): This technique allows the enrichment and augmentation of the LLM engine with locally gathered information, such as data models, other programs code or dependencies.
• Interactive AI Interfaces: Developers can interact with the solution in real-time, asking for clarifications or more detailed insights.

Now, if you’ve made it this far, I can already hear what you might be thinking: “Oh great, another case of throwing code into an LLM, adding RAG, and hoping for the best.” I get it—this kind of talk can sound like just another buzzword-filled solution. But here’s the thing: what really made this project work wasn’t just plugging into AI and crossing our fingers.

The real magic happened through careful prompt engineering and fine-tuning. We spent a lot of time making sure the solution was flexible enough to handle a wide variety of scenarios and pull in data from different sources—all while staying accurate. We also put significant effort into how the local context was assembled in RAG, ensuring that the tool could effectively draw from relevant information and deliver precise, context-aware responses. This wasn’t just about automation for the sake of it. It was about creating a tool that actually understands what it is working with and provides meaningful, actionable results.

Overcoming Challenges Along the Way

Of course, not everything went perfectly. Like any complex project, we faced a few tricky challenges along the way, and the results weren’t always flawless. For example, fully understanding the intricacies of COBOL logic sometimes required multiple interactions with the AI bot—asking follow-up questions, refining questions, and digging deeper to unravel the business rules embedded in the code.

But here’s the key: even in those moments, CoBot provided invaluable assistance. Instead of leaving developers to sift through thousands of lines of cryptic legacy code on their own, the CoBot served as a guide, speeding up the process and reducing the manual effort needed to uncover the logic. While it may not always provide a perfect answer right away, it gets you closer—faster—than traditional methods.

And this iterative process has proven to be a huge asset in modernization initiatives. It empowers teams to move forward with confidence, knowing they can efficiently extract the insights they need, even from complex, legacy systems. The result? A smoother modernization journey with far fewer roadblocks.

That said, it’s important to note that CoBot isn’t here to replace COBOL developers altogether. Instead, it significantly reduces the dependency on scarce COBOL expertise by acting as a powerful support tool. Developers can leverage the assistant to handle routine analysis and code explanation, allowing experienced COBOL professionals to focus on more complex, high-value tasks. This shift optimizes resources and eases the pressure of finding specialized COBOL talent.

Now, there’s still an elephant in the room. Can you spot it?

That’s right—data privacy and compliance! Just how comfortable are financial institutions with letting their sensitive code run through cloud environments? The answer, as you probably guessed, is not very much—and in some cases, not at all.

So, how do we tackle that challenge? Now that I’ve got your attention, stay tuned—We’ll be diving into that in our next article, where we’ll explore the role of private LLMs and how they’re shaping the future of secure AI implementations in financial services (and how CoBot took advantege of them).

Ready to Learn More?

I’d love to hear how you are tackling legacy code challenges. Leave a comment or reach out to explore how we can help your team modernize more efficiently.

References:

Importance and Application of COBOL in Banking Sectors

COBOL Blues