Many core banking and payment systems still run on technologies introduced decades ago. While these systems remain operationally critical, maintaining and evolving them has become increasingly difficult, costly, and dependent on institutional knowledge held by a small number of specialists.
That expertise is disappearing.
At the same time, modernization pressure is accelerating. DORA is reshaping operational resilience requirements. Customers expect real-time digital services. Payment ecosystems are evolving toward new standards and architectures.
AI is fundamentally changing how quickly legacy systems can be understood, documented, and modernized and reducing the operational risk of acting too late.
AI is changing how legacy banking systems can be understood. Codebases built in COBOL, assembler, and other aging technologies can now be analyzed faster, with undocumented business logic, dependencies, and service boundaries made visible for modernization teams
Without this: modernization planning is guesswork. With it: you know what you are dealing with before you write a line of new code.
Once business logic is mapped, AI accelerates the modernization process by translating legacy code, generating modern equivalents, identifying edge cases, and producing documentation alongside the code itself.
The speed advantage is significant, but good results depend on expert engineers guiding AI throughout the process: shaping prompts, steering decisions, and validating outputs against the operational, regulatory, and architectural constraints of financial systems.
G+D Netcetera standard: All AI-generated output is guided and reviewed by engineers with expertise in payment systems, banking compliance, and financial data governance.
AI generates synthetic test data and creates unit, integration, and regression tests automatically, enabling comprehensive validation without exposing production data.
For systems operating under GDPR and PCI-DSS requirements, this removes one of the biggest constraints in traditional testing workflows.
Result: Higher test coverage, faster validation cycles, and no regulatory exposure from handling production test data.
One of the biggest risks in legacy modernization is losing the operational knowledge held by the engineers who built the system.
AI helps generate continuously updated documentation directly from code analysis, capturing how systems actually behave as they evolve over time.
Result: Critical system knowledge becomes transferable, searchable, and maintainable, reducing long-term dependency on individual specialists.
We analyze the existing systems, including the codebase, architecture, data flows, integration dependencies, and business logic accumulated through years of operational exceptions and adaptations.
AI accelerates this analysis from years to weeks. The result is a documented view of how the system works, often the first complete picture the institution has ever had.
Deliverables: Business logic documentation, dependency mapping, and migration roadmap options
Based on the analysis, we define a modular modernization roadmap identifying which components should be modernized, which should remain stable, and which can be retired entirely.
Each phase is designed to deliver measurable value independently, allowing banks to realize operational benefits before the overall transformation is complete.
Deliverables: Phased modernization roadmap with business case, sequencing strategy, and risk profile for each phase
New components are built and validated in parallel with the existing system. Modernized components go live incrementally, while legacy systems stay operational until replacements are fully proven. AI-assisted development supports code generation, testing, and documentation throughout.
Security and compliance: PCI-DSS and DORA validation integrated into every phase
Deliverables: Production-ready components delivered incrementally throughout the program
We operate the systems we build under the same security and resilience standards required of critical financial infrastructure.
Because the system was documented from the beginning, operational knowledge remains embedded in the platform itself rather than concentrated in individual teams.
Operations: SLAs aligned with DORA operational resilience requirements
Deliverables: Modernized operational systems with continuously maintained documentation
Code analysis, testing, migration support, and documentation generation all operate within controlled environments, with outputs continuously validated by engineers experienced in PCI-DSS, DORA, eIDAS 2.0, and regulated banking architectures.
This is not a traditional delivery model with AI added on top. Security, resilience, and compliance are integrated into the engineering process itself.
Our advantage is not simply the use of AI. It is the combination of AI-enabled delivery with decades of experience building and operating payment, banking, and digital identity systems under real-world regulatory and operational constraints.
AI and machine learning projects in healthcare, mobility, and finance — establishing core expertise in regulated industries before the LLM wave.
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AI-assisted delivery in practice: reducing risk and manual effort in IT impact analysis for one of Switzerland’s largest banks, and building a customer-facing AI assistant for personalized financial insights at an Icelandic bank.
"The capabilities of modern AI systems are rarely the bottleneck anymore. The real challenge is making them scalable, secure, and compliant. Particularly in regulated industries like financial services. This is where G+D Netcetera's decades of experience building solutions to the highest security standards becomes a genuine advantage.”
AI-powered legacy modernization uses artificial intelligence to analyze existing banking systems, including COBOL, assembler, and other legacy codebases.
AI helps reconstruct undocumented business logic, identify dependencies and service boundaries, and support the generation of modern code equivalents. This significantly reduces the time required for analysis and migration compared to traditional manual approaches.
Yes. G+D Netcetera uses an incremental modernization approach in which systems are modernized module by module.
Each component is validated against production behavior before rollout, while the existing system remains operational throughout the process. This reduces operational risk and allows individual modernization phases to deliver value independently.
Traditional manual analysis often requires several months. AI-assisted analysis reduces this to weeks. Overall modernization timelines depend on the number and complexity of components. The phased approach allows banks to see measurable progress early in the program.
All AI tools used during delivery operate under the same governance and security standards as the production systems being modernized.
Sensitive data remains within controlled environments, and all AI-generated outputs are reviewed by engineers experienced in PCI-DSS, DORA, and European banking compliance before production deployment.
These are separate offerings.
G+D Netcetera’s AI products, including AI Banking Assistant or Impact IQ, are solutions used by financial institutions in customer-facing or operational environments.
AI-powered software delivery refers to how G+D Netcetera uses AI internally to modernize and build software more efficiently. Clients can use either capability independently or combine both.
In 2025, G+D Netcetera published a joint study with Giesecke+Devrient, appliedAI, and WeAreDevelopers on the impact of AI in software engineering.
One key finding was the shift toward smaller AI-augmented engineering teams, with many organizations moving to more compact teams supported by AI agents. The study also found that 64.8% of respondents believe new engineering competencies will be required.
The report is available for download on the G+D Netcetera website.
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