Complete Transformation of a COBOL Fraud Detection System to Java

About the Client

The client is a leading European regional bank. The client’s fraud detection system, built on COBOL, faced technical limitations and struggled to meet modern demands. It lacked the capacity to handle the growing transactional volume and integrate new technologies like machine learning models for advanced fraud detection. Outdated infrastructure produced slow processing times, limited flexibility, and growing maintenance costs. 

Goal

The client planned to modernize legacy software and expected to enhance detection accuracy, improve performance, and integrate it with modern solutions:

• Transform COBOL-based fraud detection system to Java using a scalable and modular architecture.
• Improve the system’s real-time processing speed and integrate advanced machine learning models.
• Simplify maintainability, reduce operational costs, and establish a scalable foundation for future growth.

Work Description

1. Stakeholder alignment through initial negotiations.
   1. Geomotiv’s team brought the key stakeholders on board from the very start of our cooperation. We explained why a complete rewrite was beneficial and supported it with well-founded arguments: improved scalability, easier maintenance, and enhanced fraud detection mechanisms.
   2. We agreed on project estimations, including budget, timelines, and expected impact. The team ensured that each interested party understood the project’s strategic importance.
2. In-depth legacy system analysis.
   1. The development team analyzed the fraud detection system’s components and studied its core features, including rule-based detection, data aggregation, and alert generation.
   2. Our developers explored the key workflows and integration points and prioritized the features with the most potential to deliver business value (e.g., real-time monitoring and advanced analytics integration).
3. Architecture redesign.
   1. The team designed a modular architecture that could scale without roadblocks. We used a microservices approach to separate each component for consecutive development, testing, and scaling.
   2. Our specialists planned out the infrastructure with scalability in mind. Modern architecture could also integrate emerging technologies, such as AI/ML models, to enhance fraud detection.
4. Tech stack selection.
   1. We picked Java due to its robust ecosystem, scalability, and compatibility with modern frameworks. The project also required cloud-native services to support real-time data processing and future integrations.
   2. The team shortlisted additional technologies that would be instrumental in integrating with new services and third-party tools. We also landed supporting tools to orchestrate on-demand scalability, security, and data integrity.
5. Migration strategy.
   1. Our developers designed a comprehensive ETL plan to migrate existing and real-time transaction data into the new system. We established rigorous validation and testing pipelines to maintain data integrity during migration.
   2. Historical data transition to the modernized environment was also crucial for consecutive trend analysis and retrospective fraud detection.
6. Incremental development and testing.
   1. The team adopted an iterative Agile approach that involved small incremental builds, ongoing incorporation of real-time feedback, and introduction of quick changes.
   2. Validating components and ensuring they met modern speed, accuracy, and reliability standards was also crucial. The team provided extensive testing coverage, including automated unit tests, integration tests, and performance benchmarks.
7. API and integration layer.
   1. The team delivered a robust API layer to connect a modernized fraud detection system with other touchpoints, including transaction processing platforms and customer data management systems.
   2. Another critical task was ensuring backward compatibility with a legacy system. This setup helped eliminate operational disruptions and avoid the risk of downtime.
8. Parallel rollout and validation.
   1. Our specialists deployed the new Java-based system alongside the existing COBOL system. They ran concurrently to validate real-time transaction processing speed, fraud detection accuracy, and system stability.
   2. Next, we compared the outputs of the two systems to identify and resolve discrepancies. The goal was to ensure the new system matched or exceeded the performance of the legacy system.
9. Cutover and deployment strategy.
   1. The developers followed an incremental cutover process. They started by releasing less critical transaction types and extended reach to full deployment across all transaction types.
   2. Comprehensive backup and rollback plans were put in place to address any detected issues.
10. Post-deployment monitoring and support.
    1. We set up continuous monitoring pipelines to track the system’s health and respond to any identified issues in real-time. 
    2. Our post-deployment support tasks included optimizing fraud detection algorithms, tuning their performance regularly, and continuously updating them based on revealed fraud patterns.
11. Knowledge transfer and documentation.
    1. Our developers provided detailed documentation to the client, including thorough walkthroughs of the architecture, data flows, and integration points.
    2. We conducted hands-on training sessions with the client’s in-house teams, supporting them in internally managing and maintaining the new system.

Results

• 50% fraud detection speed uplift, streamlining real-time transaction processing and fraud detection.
• There is a 30% decrease in operational costs by lowering maintenance overhead and replacing resource-intensive software with a microservices-based system.
• Seamless integration with modern analytics tools and machine learning models for advanced fraud detection, which boosted accuracy and reduced false positive results.