Banking Mainframe Modernization: Transforming Credit Line & Collateral Architecture
How a leading European bank partnered with us to overcome mainframe limitations through an innovative event-driven architecture, reducing query response times from minutes to sub-second while enabling greater flexibility and significant cost savings.
Customer Context
Our client is one of the biggest European banks, operating with traditional mainframe systems to support their core banking operations. The institution provides credit line approvals and creditor position assessments across their customer base.
As a major financial player in Europe, the bank manages complex financial data and transactions that require comprehensive evaluations for credit decisions and risk management.
The Challenge
While Mainframe systems provide stability and reliability, they also introduce significant challenges that are becoming increasingly difficult to ignore:
Beyond the financial burden, mainframes also impose rigid constraints on data architecture. Their relational database structure limits banking system flexibility, making it difficult for banks to adopt modern data models such as JSON-based document storage. This lack of adaptability slowed down banking innovation enablement and made integrating new services cumbersome.
Operational complexity is another challenge. Maintaining a mainframe-dependent database requires direct system access, increasing reliance on specialized skills that are becoming harder to find. Any evolution in data models often necessitates software development expertise, creating bottlenecks in IT operations. Moreover, mainframe query performance issues further exacerbate these problems.
Querying mainframe data, especially for complex evaluations like assessing multiple creditor positions, can take anywhere between 30 and 60 seconds. These delays impact both customer experience and internal decision-making, creating friction in a world that demands real-time data access banking.
Faced with these challenges, the bank needed a solution that reduced costs, enhanced agility, and improved data accessibility—all without disrupting existing operations. Summing all these up:
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High Costs: Mainframe systems created a significant financial burden for the bank
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Rigid Data Architecture: Relational database structure limited flexibility, preventing adoption of modern data models like JSON-based document storage
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Innovation Barriers: Lack of adaptability slowed down innovation and made integrating new services cumbersome
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Operational Complexity: Maintaining mainframe-dependent databases required direct system access and specialized skills becoming harder to find
- Development Bottlenecks: Data model changes often necessitated software development expertise, creating IT bottlenecks
- Performance Issues: Complex queries (like assessing multiple creditor positions) took 30-60 seconds to execute
- Responsiveness Gaps: Slow performance impacted both customer experience and internal decision-making in a market demanding real-time responses
- Modernization Needs: The bank required a solution that reduced costs, enhanced agility, and improved data accessibility without disrupting operations
The Solution
Agile Lab's Bespoke Banking Architectural Solution
The answer lies in rethinking how data is managed and accessed, adopting an event-driven architecture for banking that operates independently while remaining synchronized. By implementing a real-time system based on Change Data Capture banking, banks can dramatically reduce MIPS-based costs while unlocking new capabilities.
Instead of continuously querying the mainframe to retrieve information, the system captures every change in real time and stores the latest data state in a document-based model. This eliminates the need for direct interactions with the mainframe, significantly lowering operational costs. The transition to a JSON document data model allows for more flexible data structures, making it easier to evolve over time without the rigid constraints of relational databases.
Since the evolution of these documents can be defined declaratively, adapting the system does not require complex software development, further simplifying operations.
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Another advantage of this approach is the elimination of credit decision latency. Previously, retrieving a complete financial picture of a creditor required running a query on the mainframe, resulting in long wait times. Now, banks can simply access the most recent version of the corresponding JSON document, which is automatically updated as soon as a transaction occurs through efficient banking data synchronization. This feature dramatically improves response times. Complex evaluations that once took up to a minute can now be completed in under a second, enhancing both customer interactions and internal processes.
The benefits of this transformation extend beyond banking. Any financial institution, including insurance companies and large enterprises running expensive relational databases, can leverage this solution to reduce costs and improve efficiency. As long as a CDC mechanism is in place, the system ensures continuous synchronization between the mainframe and the new architecture, allowing organizations to modernize without compromising business continuity.
Real-World Impact and Benefits for Banking & Financial Services Organizations
| Operational Area | Before CDC Mechanism | With CDC Mechanism |
|---|---|---|
| Query Performance | Complex evaluations took 30-60 seconds on mainframe | Same queries completed in under 1 second |
| Credit Approval Process | Lengthy approval times with excessive paperwork | Approval within minutes in simplest cases with minimized paperwork |
| Data Accessibility | Direct mainframe access required, creating bottlenecks | Real-time access to JSON documents without mainframe interaction |
| Cost Structure | High MIPS-based mainframe operational costs | Significantly reduced infrastructure costs through eliminated direct queries |
| Data Model Flexibility | Rigid relational database structure requiring development for changes | Flexible JSON-based document model with declarative evolution |
| Operational Focus | Resources dedicated to bureaucratic and technical tasks | Shifted focus to understanding and addressing client needs |
| Risk Management | Limited by slow access to comprehensive creditor data | Improved access to essential credit assessment data reducing operational risks |
| System Adaptability | Difficult integration of new services and innovations | Enhanced agility to adapt to new business needs without disrupting workflows |
Conclusion
By decoupling data access from the mainframe and adopting a real-time, event-driven architecture, banks can significantly reduce infrastructure costs while improving operational agility. The adoption of a JSON-based document model provides the flexibility needed to adapt to new business needs without disrupting existing workflows. At the same time, real-time data synchronization ensures that decision-making is no longer constrained by slow mainframe queries.
The line of credit line approval is just 20 minutes in the simplest cases and minimizes the paperwork required from clients, creating a more efficient and seamless experience. This improved access to essential credit assessment data not only enhances decision-making speed but also reduces operational risks. As a result, the bank shifted focus from bureaucratic tasks to what truly matters — understanding and addressing the clients’ unique needs.
More than just a technical upgrade, this approach represents a strategic direction towards a more agile, cost-efficient, and data-driven financial ecosystem.