Articles
July 1, 2025Confident Core Modernization: A Blueprint for Banking Transformations
Laying the Foundation: Assessing Architecture and Products
Choosing the Right Core Strategy
Sequencing Your Transformation

Lower initial risk
Avoids immediate migration of large data volumes, reducing disruption.

Faster time-to-market
Enables launch of new products on a modern platform without impacting legacy customers.

Performance validation
Tests scalability and reliability in real-world conditions while surfacing issues before full migration.

Easier rollback
Migration can be paused with minimal impact if challenges arise.

User-driven refinement
Feedback from early users helps improve onboarding flows, product features, and system usability.

Dual system complexity
Running two cores in parallel increases operational costs and complexity. Transactions that span both systems—such as transfers between a legacy and a modern account—require complex reconciliation logic to maintain accuracy, auditability, and reduce impacts to downstream data streaming or reporting.

Fragmented customer experience
Front-book first modernizations can create a two-tiered experience where new customers benefit from modern features while legacy customers remain on outdated systems. Without careful orchestration, this can lead to inconsistent functionality, user confusion, and dissatisfaction. However, banks can mitigate this risk through experience orchestration layers, consistent branding, and selectively backporting key features to the legacy platform.

Slower ROI
Legacy systems remain in place longer, delaying cost savings and operational efficiency gains.

Unified customer experience
All customers benefit from new features and performance improvements simultaneously.

Operational efficiency
Retiring the legacy system sooner reduces long-term maintenance and infrastructure costs.

Data consistency
Avoids the complexity of synchronizing two separate core systems, simplifying reporting and compliance.

High migration risk
Back-book migrations often require a large-scale cutover, where data is frozen, extracted, transformed, and validated. This process is complex and error-prone, and failures can lead to outages, inaccessible accounts, and regulatory exposure.

Data quality challenges
Legacy systems often contain decades of inconsistent or incomplete data. Back-book data must be cleansed and accurately mapped to modern schemas aligned with current definitions and validation rules.

Delayed time-to-value
The benefits of modernization may take longer to realize due to the scale and complexity of the migration effort.
Client Stories

From Planning to Cutover: A Historic Mainframe Exit

Scalable Data Modernization
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Anna Hegeler
Manager, Management Consulting
Anna is a product management consultant with over seven years of experience in digital transformations and delivering customer-centric products and technology across financial services, government, and logistics. Known for simplifying complex topics and challenges into actionable strategies, Anna is passionate about understanding customers and exploring new technologies.