Incremental Modernization of Loan Management System from Delphi to Python

About the Client

The client is a national banking institution. The company operated a Delphi-based loan management system that attracted a growing number of clients expecting streamlined loan approval processes. Legacy software required transformation to meet the increased demand for uninterrupted services, facilitate quick integration of new features, and reduce operational costs.

Technologies Used

Goal

The client planned to modernize the system by introducing incremental improvements and maintaining uninterrupted operations during migration:

• Replace the critical components step-by-step with a scalable Python-based architecture.
• Improve system performance to accelerate loan processing and approvals.
• Enable easy integration with cloud-based tools and third-party services during the transition period.
• Maintain uninterrupted operations by minimizing downtime and eliminating risks associated with migration.

Work Stages

1. Initial assessment and planning
2. Migration strategy definition
3. Modularization and prioritization
4. Modular architecture design
5. Step-by-step data migration
6. Incremental development and testing
7. Legacy system integration via APIs
8. Parallel running and validation
9. Incremental rollout and user training
10. Post-migration optimization and improvement
11. Full transition over time

Work Description

1. Initial assessment and planning
   • The team identified critical components that were indispensable for core business operations, including loan origination, processing, and reporting.
   • We collaborated with the project’s stakeholders to align on a phased development approach. The team suggested introducing incremental changes instead of overhauling the system at once, which would reduce risks and minimize system downtime.
2. Migration strategy definition
   • Our specialists prepared a modular architecture design blueprint to replace Delphi components with Python modules progressively.
   • The team provided a detailed roadmap, complete with prioritized phases for replacing components.
   • We collected the list of APIs and adapters necessary for seamless connection between legacy and new components.
3. Modularization and prioritization of components
   • Our developers broke down the system into isolated components to modernize and test each element without disrupting the rest of the operations.
   • We prioritized the most critical modules for early iterations. It was decided to start with loan origination and processing, as we found them to have the most significant impact on performance and UX.
4. Modular architecture design
   • The team designed modular architecture for consecutive integration of new Python components into existing Delphi parts.
   • We implemented microservices patterns where applicable to isolate the development, testing, and deployment of modernized modules.
5. Step-by-step data migration
   • Our developers transitioned critical data elements as modular segments to ensure data integrity and consistency between legacy and modern environments.
   • We deployed automated ETL scripts for data extraction, transformation, and loading. To narrow technical risks, rigorous validation checks were rolled out at each stage.
6. Incremental development and testing
   • The agile approach to development orchestrated incremental deployment of new components and enabled continuous feedback collection.
   • Automated testing frameworks (unit, integration, and performance testing) helped adhere to rigorous quality and performance standards.
   • The team conducted parallel running and testing of old and new systems to verify data integrity and new functionality.
7. Legacy system integration via APIs
   • The team developed APIs to enable seamless data exchange between legacy software and modernized Python modules during the transition period.
   • We established an API facade to pave the way for consecutive integrations and reduce dependence on the outdated system.
8. Parallel running and validation
   • Our development team deployed new Python modules and the Delphi-based system concurrently to validate the consistency of outputs across two environments.
   • The developers compared the results of legacy and modernized modules to detect and resolve any discrepancies without intervening in ongoing operations.
9. Incremental rollout and user training
   • The team released new components incrementally, followed by training sessions and dedicated support from our developers.
   • We collected user and stakeholder feedback to make informed decisions regarding upcoming modernization efforts.
10. Post-migration optimization and improvement
    • Our specialists assessed new modules’ performance in real time and optimized the architecture accordingly.
    • We focused on adjusting the software’s scalability, efficiency, and security as more features were modernized.
11. Full transition over time
    • The team contributed to the gradual retirement of all Delphi components by rewriting and deploying each module in Python.
    • Once this legacy system was completely retired, the team focused on continuous improvements and introducing new features within a modern architecture.

Results

• 35% acceleration of loan processing speed, which led to quicker loan approvals and improved customer satisfaction.
• Over 20% reduction in operational costs due to lower maintenance requirements and fewer system errors.
• Seamless integration with cloud-based analytics tools and third-party services, which facilitated data-driven insights and improved loan management capabilities.
• Successful step-by-step migration that didn’t interrupt business operations throughout the process.