How to de-risk core banking modernization (and move faster)

Most regional banks and credit unions allocate 62-65% of IT budgets to maintaining legacy systems—and in reality, it's often higher. Meanwhile, 75% of financial institutions expect to integrate AI capabilities, but their infrastructure blocks them at every turn.

Your legacy core creates an impossible choice: modernize and risk catastrophic failure, or keep burning budget on maintenance while competitors ship AI-powered fraud detection and instant payments.

The traditional answer—rip out the core and replace it—costs $20M+, takes 3-5 years, and puts your entire operation at risk during cutover. But there's a faster path that mid-market institutions are using: build modern APIs around legacy systems, launch AI-ready features immediately, then gradually sunset old infrastructure as new services prove stable.

Why core replacement projects fail (and what works instead)

Big-bang replacements fail because they attempt to migrate 30 years of business logic, customer data, and regulatory compliance in one massive cutover. The blast radius is 100% of your operations.

The incremental approach limits risk by isolating blast radius to 5-10% of operations per change instead of betting everything on one cutover weekend. Instead of replacing your core, you:

1. Document how critical systems actually work: Map dependencies, identify what will break, create a risk-scored modernization roadmap
2. Build modern APIs and microservices around legacy infrastructure: Create new services that interface with core functions so applications can access data without touching old code
3. Launch customer-facing features in weeks: Mobile banking, real-time payments, AI fraud detection—all built on cloud-native infrastructure while legacy systems keep running
4. Gradually modernize underlying systems: Once new services prove stable, sunset corresponding legacy components with minimal risk

This approach delivers modern customer experiences immediately while buying time to modernize the foundation safely.

Phase 1: Document dependencies before touching anything

The reason CTOs fear modernization is simple: nobody knows what will break. Your senior engineers hold critical system knowledge in their heads, but they're retiring. Documentation is outdated or missing entirely.

Before changing anything, document:

• How core systems actually work today
• Critical dependencies between services
• Data flows across siloed systems
• Regulatory compliance touchpoints
• Single points of failure

This audit becomes your competitive advantage. You identify:

• Which systems can't support real-time fraud detection
• Where customer data is trapped in silos that block AI/ML training
• Which compliance processes rely on manual intervention instead of automated monitoring

With state-level enforcement up 72% and 18 states now enforcing conflicting regulations, documentation isn't just about modernization—it's about proving compliance readiness when examiners ask questions.

This creates a risk-scored roadmap that shows exactly which modernization steps are safe and which require extra caution. You eliminate the "we don't know what we don't know" problem that kills most modernization projects.

Phase 2: Build AI-ready infrastructure layer

With dependencies mapped, build REST APIs and microservices that interface with legacy core functions. This layer becomes the foundation for all new applications—mobile apps, payment processing, fraud detection systems.

The genius of this approach: your legacy core keeps running exactly as it does today. Nothing breaks because you haven't touched it. But new customer experiences can now launch in weeks instead of waiting years for core replacement.

For example, a regional bank can launch instant account opening by building a new microservice that:

• Collects data through a modern interface
• Calls legacy core functions to create the account
• Integrates real-time identity verification (impossible with batch-processing systems)
• Provides instant confirmation to customers

Whether your core runs on COBOL or legacy Java, it continues handling transactions—it just receives requests through modern APIs instead of overnight batch files.

This modern API layer also unlocks your data for AI and machine learning. Instead of customer transaction data trapped in COBOL batch files, you now have real-time access to structured data that can feed:

• Fraud detection models
• Personalization engines
• Predictive analytics

All without touching the core systems that process the actual transactions. Whether you're building real-time payment rails via FedNow, deploying AI-powered fraud detection, or launching mobile banking, this layer becomes the foundation that makes it all possible.

Phase 3: Incrementally replace legacy components

Once the modern layer is stable, begin replacing legacy components one service at a time. Start with non-critical systems to prove the approach, then tackle core functions.

The key: blast radius stays small. If a replacement fails, only 5-10% of operations are affected instead of your entire bank. You can roll back, fix issues, and try again without catastrophic consequences.

This process also builds institutional knowledge, moving away from "tribal knowledge" toward a maintainable, modern engineering culture.

This is how you modernize 30-year-old financial systems without betting your institution's reputation on a single cutover weekend.

Capabilities that matter for AI-readiness in financial services

Modernization isn't just about replacing COBOL. It's about building the infrastructure that lets you compete with digital-first challengers:

Unified data architecture: Customer transactions, account data, and behavioral signals consolidated into a single source of truth that can feed ML models and real-time analytics. No more data trapped in siloed batch systems.

Real-time payment rails: 24/7/365 operations supporting FedNow and RTP integration with instant fraud detection. Legacy batch processing becomes the fallback, not the primary path.

AI-powered fraud detection: Behavioral analytics that protect transactions in milliseconds instead of flagging suspicious activity hours later in batch runs.

Compliance automation: RegTech platforms that adapt to evolving state regulations automatically instead of manual processes that lag behind enforcement changes.

Modern security architecture: Zero-trust security, continuous monitoring, and AI-powered threat detection without touching fragile core systems.

These capabilities are table stakes for competing in 2026 and beyond. The incremental approach lets you build them on modern infrastructure while your legacy core keeps running—then sunset the old systems once the new ones prove stable.

The cost of waiting (and how to start safely)

Every year you delay modernization, technical debt compounds. Senior engineers retire. Competitors ship features faster. Compliance costs rise. And the eventual modernization becomes more expensive and risky.

Your competitors aren't waiting. Digital-first challengers launched with AI-native infrastructure. Traditional banks with deeper pockets are spending their way to modernization. Mid-market institutions that act now can build AI-ready infrastructure incrementally, launch competitive features fast, and modernize safely without Big 4 budgets or timelines.

The incremental approach lets you modernize gradually while delivering customer value immediately. You don't have to choose between safety and speed—you can have both with the right approach and team.