Customer Stories
Global Bank Reduces Analysis Effort by 70% using AI-Enabled Legacy Modernization
Using Xebia Ace™, the bank uncovered 25 years of undocumented business logic in weeks, cutting legacy analysis effort by up to 70% and creating a trusted foundation for modernization.
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At a Glance
Challenge
Modernize legacy portfolio and model management systems while preserving critical business logic embedded within a 25-year-old platform.
Solution
AI-enabled legacy modernization accelerator powered by Xebia Ace™ and Claude Opus 4.5.
Results
60–70% faster legacy analysis.
Backlog readiness achieved within weeks.
Lower dependency on scarce legacy SMEs.
Reduced migration and delivery risk.
Stronger business and IT alignment.
The Client
A US-based financial institution offering a complete digital banking portfolio, including personal banking, business banking, and wealth management services through a low-fee, digital-first operating model.
The Challenge: Extracting Critical Institutional Knowledge from a 25-Year-Old System
The bank launched a strategic initiative to modernize its portfolio and model management capabilities. The program involved moving from legacy ALF and Liberty platforms to Bank Universal Core while creating a more consistent advisor experience through Access Professional Workstation (APW).
The goals were clear: improve block trading workflows, simplify model rebalancing, support cross-model allocations, and provide advisors with greater visibility into sub-account activity. The technology roadmap was already in place. The challenge was understanding exactly how the legacy platform behaved before rebuilding it.
The bank had working demos of the future-state platform. But demos only show intended functionality. The business logic that powered day-to-day operations lived deep inside the existing system.
Over 25 years, business rules, validations, allocation logic, exception handling, and batch processes had accumulated inside the ALF platform. Much of that knowledge existed only in code that wasn't easy to interpret.
Several factors increased complexity and risk:
- Critical business rules, validations, and algorithms were undocumented.
- The underlying Pick Basic architecture and multi-valued file structures required specialized knowledge to analyze.
- A limited pool of Pick Basic experts created continuity risks.
- Existing documentation was extensive but fragmented across systems and teams.
- Missing even a small piece of business logic could introduce migration defects, compliance issues, operational disruption, or costly rework.
The bank needed a faster way to discover, validate, and document legacy system behavior before development could move ahead.
The Solution: Accelerating Requirement Discovery with AI-Assisted Analysis
Xebia deployed its AI-enabled legacy modernization accelerator to uncover and document business-critical knowledge embedded within the ALF platform. Using Xebia Ace™, an AI-Native digital engineering framework and Claude Opus 4.5, the team analyzed ALF subroutines feature by feature. Structured prompts, reusable templates, and review guardrails helped maintain consistency across thousands of lines of legacy code.
Xebia’s approach included:
- Grouping ALF subroutines by business capability to create an end-to-end view of platform behavior.
- Using Xebia Ace™ and Claude Opus 4.5 to systematically extract business logic through governed analysis workflows.
- Documenting business rules, workflows, validations, algorithms, exception handling patterns, and data relationships hidden within legacy code.
- Converting findings into FRD-ready artifacts that supported backlog preparation and stakeholder reviews.
The engagement produced a structured knowledge base that included:
- Business rule catalogs
- Workflow documentation
- Batch process logic
- Validation and algorithm definitions
- Exception handling patterns
- Relational schema designs
- Data mapping specifications
- Integration touchpoint documentation
For the first time, business teams, architects, and engineers were working from the same fact base.
The Results: Faster Discovery, Better Alignment, Lower Risk
The bank established a clear understanding of existing platform behavior and created a trusted foundation for the next phase of modernization. The result was a modernization program built on evidence rather than assumptions.
Key outcomes:
- Faster modernization readiness: Backlog readiness was achieved within weeks rather than months.
- 60-70% Reduction in Analysis Effort: Legacy analysis efforts dropped by 60-70% compared with traditional manual approaches.
- Stronger Business and IT Alignment: Documented system behavior gave stakeholders a common reference point for requirements, decisions, and prioritization.
- Lower Migration Risk: Greater visibility into business logic reduced the likelihood of migration defects, compliance gaps, and downstream rework.
- Reduced SME Dependency: Critical institutional knowledge was captured and documented, decreasing reliance on legacy platform experts.
Looking Ahead
The bank now has a documented understanding of the business logic that underpins its portfolio management operations. With that foundation in place, teams can progress through the Bank Universal Core and APW modernization roadmap with fewer unknowns, faster decision-making, and a clearer path to delivery.
Explore More
Many modernization programs slow down long before development begins. Teams spend months trying to understand undocumented business logic, validate assumptions, and fill knowledge gaps left by aging platforms.
Xebia helps organizations turn legacy systems into usable knowledge, giving modernization teams the clarity they need to move faster and reduce delivery risk.
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