Combined Credit Control System

Project Overview

The Combined Credit Control project was a large-scale enterprise initiative at Allianz Technology, aimed at consolidating multiple legacy credit control systems into a single, modern, scalable platform serving millions of users across multiple markets.

The Challenge

Allianz had multiple disconnected credit control systems across different markets and business units:

• Legacy Systems: Multiple outdated systems with different technologies
• Data Inconsistency: No unified view of customer credit information
• Scalability Issues: Systems couldn't handle growing transaction volumes
• Regulatory Compliance: Need for enhanced reporting and audit capabilities

Solution Architecture

Technology Stack

• Backend: Java with Spring Boot framework
• Microservices: Spring Cloud for distributed systems
• Database: PostgreSQL for ACID compliance
• Infrastructure: Azure with Kubernetes orchestration
• Message Queue: Apache Kafka for event streaming
• Cache: Redis for session and data caching

Key Features Delivered

1. Unified Credit Assessment Engine

• Real-time credit scoring algorithms
• Machine learning models for risk assessment
• Integration with external credit bureaus
• Automated decision workflows with business rules

2. Scalable Microservices Architecture

• Service-oriented architecture with clear domain boundaries
• Event-driven communication between services
• Circuit breakers and retry mechanisms for resilience
• Centralized configuration management

3. Real-time Dashboard & Reporting

• Executive dashboards with KPI visualization
• Real-time monitoring of credit portfolios
• Automated regulatory reporting capabilities
• Custom report builder for business users

Technical Achievements

Performance Improvements

• Response Time: Reduced from 8-12 seconds to <2 seconds
• Throughput: Increased processing capacity by 300%
• Availability: Achieved 99.9% uptime SLA
• Data Processing: Handle 1M+ transactions per day

Security & Compliance

• GDPR Compliance: Full data protection implementation
• SOX Compliance: Complete audit trail and controls
• Security: Multi-layer security with OAuth 2.0 and JWT
• Data Encryption: End-to-end encryption for sensitive data

Implementation Challenges & Solutions

Challenge 1: Data Migration

Problem: Migrating 10+ years of credit data from legacy systems Solution:

• Built custom ETL pipelines with data validation
• Implemented zero-downtime migration strategy
• Created data reconciliation tools

Challenge 2: System Integration

Problem: Integration with 50+ internal and external systems Solution:

• Designed API gateway with rate limiting
• Implemented event-driven architecture
• Created standardized integration patterns

Challenge 3: Performance Under Load

Problem: System needed to handle peak loads during business hours Solution:

• Implemented horizontal scaling with Kubernetes
• Used caching strategies with Redis
• Optimized database queries and indexing

Results & Impact

Business Impact

• Cost Reduction: 40% reduction in operational costs
• Processing Time: 60% faster credit decisions
• Accuracy: 40% improvement in risk assessment accuracy
• User Satisfaction: 90%+ user satisfaction score
• Regulatory: Zero compliance issues since launch

Technical Metrics

• Code Quality: Maintained 95%+ test coverage
• Performance: Sub-2-second response times maintained
• Reliability: 99.9% uptime achieved consistently
• Scalability: Successfully handling 300% increased load

Team Leadership

As Senior Lead Developer and Architect, I was responsible for:

• Technical Leadership: Led a team of 12 developers
• Architecture Design: Created scalable microservices architecture
• Code Quality: Established coding standards and review processes
• Stakeholder Management: Regular communication with business stakeholders
• Mentoring: Guided junior developers and conducted knowledge sharing sessions

Key Technologies & Tools

• Java & Spring: Core backend development with Spring Boot and Spring Cloud
• Microservices: Service mesh with Azure Service Fabric
• Azure Cloud: Full cloud-native deployment with AKS
• Kubernetes: Container orchestration and auto-scaling
• PostgreSQL: Primary database with read replicas
• Apache Kafka: Event streaming and message processing
• Redis: Caching layer for improved performance
• Docker: Containerization for consistent deployments

Future Enhancements

The system was designed with extensibility in mind, enabling future enhancements:

• AI Integration: Enhanced machine learning models for risk assessment
• Real-time Analytics: Advanced predictive analytics capabilities
• Mobile Integration: API support for mobile applications
• Partner Ecosystem: Public APIs for third-party integrations

This project demonstrates expertise in enterprise-scale system architecture, Java/Spring development, cloud technologies, and leading technical teams in regulated financial services environments.