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Centralized Banking System Data Migration with Real-Time Validation
  1. case
  2. Centralized Banking System Data Migration with Real-Time Validation

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Centralized Banking System Data Migration with Real-Time Validation

itmagination.com
Financial services
Loans & Credit
Capital Markets

Legacy System Integration Challenges

Need to consolidate 3.2M+ credit accounts and 1.3M+ credit cards from 9 disparate source systems into a centralized banking platform while maintaining data integrity, ensuring regulatory compliance, and minimizing operational disruption during migration.

About the Client

Member of international banking group offering consumer finance products (loans, credit cards) through 12,000+ outlets and agriculture-focused financial services

Modernization Goals

  • Migrate 4B+ records across 18 data flows within 13 months
  • Achieve 99.9997% data accuracy with <0.0004% rejection rate
  • Implement real-time reconciliation dashboard for migration quality monitoring
  • Ensure seamless integration with existing debt collection and client management systems

Core System Capabilities

  • Automated ETL pipeline with error detection/correction
  • Real-time reconciliation dashboards with 650+ metrics
  • Data quality assurance engine with exception handling
  • Multi-system integration framework (9 sources → 4 targets)
  • Audit trail generation for regulatory compliance

Technology Stack

Microsoft SQL Server
CLIO migration solution
ETL frameworks

System Interoperability

  • Legacy banking systems (9 instances)
  • Central banking platform (4 target systems)
  • Client relationship management (CRM) systems
  • Debt collection platforms

Operational Requirements

  • 99.99% system availability during migration
  • Scalable architecture for 10M+ daily transactions
  • Data encryption at rest/in transit (PCI-DSS compliant)
  • Disaster recovery with <15min RTO

Business Transformation Outcomes

Enables unified customer view across all channels, reduces operational costs by 40% through system consolidation, improves regulatory compliance with real-time audit capabilities, and establishes foundation for AI-driven credit risk modeling while maintaining PLN 216-level reconciliation accuracy at multi-billion balance sheet scale.

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