Russ Brown Jan 13, 2026
A colleague of mine – Ellison Chan recently shared an exciting AI project he’s working on: using large language models (LLMs) and generative AI to modernize legacy COBOL code. It’s a smart, timely idea — and yes, there’s a very real and rapidly expanding market for this in financial services (banks, insurance companies, credit unions, payment processors, and more).This isn’t hype; it’s one of the hottest enterprise AI applications right now (as of early 2026). Here’s why it’s gaining serious traction, backed by real industry momentum.
Financial institutions still run enormous amounts of mission-critical code in COBOL — the language from the late 1950s that’s powering core systems like transaction processing, accounts, loans, payments, and insurance claims.
- Scale: Globally, estimates put 220–300 billion lines of COBOL code in production, with ~43% of banking systems still built on it. In the US alone, COBOL handles trillions of dollars in daily transactions.
- Pain points:
- Talent crisis: COBOL experts are retiring (average age 50+), and almost no new developers learn it. Maintenance costs are exploding — often eating 60%+ of IT budgets.
- Technical debt: Legacy mainframes are expensive, hard to integrate with modern cloud/API/microservices, and slow innovation.
- Risk: Full manual rewrites or migrations take 5–10+ years and hundreds of millions — too slow and risky for most organizations.
Traditional modernization is painful. AI/LLMs change that by making it incremental, faster, and lower-risk.
How Generative AI Tackles COBOL Modernization
LLMs (especially code-specialized ones) shine at:
- Explaining undocumented COBOL logic in plain English
- Generating/updating documentation and comments
- Refactoring code
- Translating COBOL → Java, Python, C#, or microservices
- Extracting buried business rules
- Generating unit tests
- Identifying dependencies and vulnerabilities
This enables phased, safe modernization — update one module at a time, validate automatically, and preserve functionality while reducing complexity and costs (often 40–50% time savings, per McKinsey/Accenture reports).Real Momentum & Adoption in Financial Services (2025–2026)The space is heating up fast, with big players and real deployments:
- IBM watsonx Code Assistant for Z — Specifically built for COBOL-to-Java on mainframes. Already in use at financial institutions; it’s a flagship for mainframe clients (banks/insurers).
- Accenture, EY, Capgemini — All have GenAI tools for COBOL analysis, documentation, and translation.
- Microsoft Azure AI agents — Used for COBOL migration and mainframe modernization in banking/insurance.
- Case studies & pilots — Regional US banks/insurers use these tools; Goldman Sachs pilots assistants writing 40%+ of code; European insurers migrate complex apps with 95% latency reduction.
Living It Firsthand in Charlotte
Here in Charlotte, North Carolina — the second-largest banking hub in the United States — this issue is front and center every day. The city is home to major players like Bank of America (headquarters), Truist (significant presence), Wells Fargo (large operations), and dozens of regional banks, credit unions, and fintech firms. Many of these institutions still rely heavily on COBOL-based core systems for their day-to-day transaction processing. You can feel the urgency firsthand: IT leaders here are openly talking about the retiring workforce, skyrocketing maintenance costs, and the pressure to modernize without breaking the business. Charlotte’s banking community is actively piloting and investing in AI-assisted legacy modernization — it’s not just a national trend; it’s happening right in our backyard.
Market Size & Growth Projections
- Broader mainframe modernization: ~$8–9B in 2025 → $13B+ by 2030 (CAGR 9–10%).
- AI/GenAI legacy code segment: ~$1.8–2B in 2024–2025 → $14B+ by 2033 (CAGR 25%+).
- Application modernization services: $30B+ in 2025 → $100B+ by 2033.
- Financial services takes the biggest share (~40%), with 70%+ of large banks planning AI budget increases for modernization & compliance by 2026.
Gartner predicts 75% of software engineers will use AI code assistants by 2028 (up from <10% in 2023), with legacy modernization as a key driver.