Few things cause more sleepless nights in banking than a major system upgrade. Despite the cost, risk, and time involved, most banks know modernization is no longer optional. Yet nearly 60% of banks say legacy infrastructure is their biggest obstacle. Outdated processes only make it harder to stay competitive. But modernization is possible with the right mix of people, processes, and technology—and generative AI (gen AI) is now helping accelerate this journey.
The People: Lowering Legacy Software’s Cost
Many banks still run critical functions on legacy systems, such as COBOL-based core banking platforms. A simple change, like expanding branch IDs from three digits to four, can take months due to old code and data dependencies. The challenge is worsened by an aging workforce—many COBOL experts have retired, leaving younger IT staff with limited knowledge of these systems.
This increases the total cost of ownership (TCO). Gen AI can bridge this gap by reverse-engineering legacy code to:
- Identify dependencies
- Map out pathways and correlations
- Understand intent of legacy sections
- Highlight components for replacement
By giving teams the right AI-powered tools, banks can lower TCO and make modernization more achievable.
The Process: Strangler Fig Pattern for Incremental Change
Bank leaders often fear that system upgrades mean a full rip-and-replace, costing millions and taking years. But the “strangler fig pattern” offers a safer path. Instead of replacing everything at once, teams gradually build new components alongside existing systems. After parallel testing, the legacy parts can be retired.
This incremental approach helps banks by:
- Reducing modernization risks
- Maintaining business continuity
- Optimizing resources and costs
With this strategy, banks can move from paralysis to action without disruption.
The Tech: Gen AI Across the SDLC
Gen AI isn’t just for fixing COBOL. It can deliver 10x productivity boosts across the software development lifecycle (SDLC), including:
- Discovery and ideation
- Product design
- Backlog management
- Code analysis and generation
- Testing and quality assurance
- Deployment and release management
In practice, banks using gen AI platforms have already achieved 20%–50% productivity improvements depending on the task. This makes modernization both faster and more cost-effective.
- Key challenge: Legacy infrastructure blocks innovation for 60% of banks
- Solution: Use gen AI to reverse-engineer and maintain old code
- Approach: Strangler fig pattern for controlled upgrades
- Impact: 20–50% productivity gains across SDLC
Future-Proofing Banking Upgrades
Outdated systems have a steep TCO that banks cannot sustain. Traditional upgrade methods are inefficient, but gen AI provides a massive opportunity to revamp development processes. By integrating AI with people, processes, and technology, banks can deliver modern, efficient, and reliable digital experiences—future-proofing their operations.
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