Manufacturing & Technology
The Challenge
Modernize 20+ year old monolithic core banking system without disrupting critical financial operations
Context & Background
SecureBank operates a 25-year-old monolithic COBOL-based core banking system serving 2.3M customers. The system processes over 50,000 transactions daily and handles $12B in deposits. Critical challenges included: 78% of IT budget spent on maintenance, 14-day average deployment cycles, limited scalability, and compliance risks due to outdated security protocols. The system lacked modern APIs, had no automated testing, and required manual intervention for most operations.
Business Impact
The legacy system was costing $8.2M annually in maintenance, preventing digital transformation initiatives, and creating regulatory compliance concerns. Customer satisfaction scores were declining due to slow transaction processing and system downtime.
Project Details
- Client
- SecureBank Financial Corporation
- Industry
- Financial Services
- Service Type
- Software Reengineering
- Duration
- 12 months
- Timeline
- 12 months (4 phases)
- Team Size
- 12 specialists (4 AI/ML engineers, 3 security analysts, 3 COBOL developers, 2 DevOps engineers)
Our Approach
Methodology
ML-guided fuzzing for security analysis, binary code similarity for migration planning, SBOM-first modernization approach
Strategy
Implement incremental modernization using AI-driven code analysis and LLM-assisted refactoring. Deploy NEUZZ-style fuzzing for comprehensive testing, use CycloneDX for supply chain security, and leverage transformer embeddings for code similarity analysis.
AI Technologies & Tools
AI Technologies
ML-guided fuzzing (NEUZZ family) Binary code similarity via deep learning LLM-assisted code refactoring CFG/Transformer embeddings
Frameworks
TensorFlow/PyTorch for model training AFL++ for fuzzing CycloneDX for SBOM Docker/Kubernetes for containerization
Measurable Results
Technical Performance
Code Similarity Matching
96.4%from 67.2% (baseline)
Security Vulnerabilities Found
847342% increase vs manual testing
Test Coverage
89.7%from 23.1%
Deployment Frequency
Dailyfrom bi-weekly
Code Quality Score
8.7/10from 4.2/10
Business Impact
Annual Maintenance Cost Reduction
$6.4M78% reduction
System Uptime
99.97%from 97.2%
Transaction Processing Speed
340ms67% faster
Developer Productivity
+145%faster feature delivery
Time to Market
8 weeksfrom 24 weeks
Project Outcomes
Technical Achievements
- Successfully migrated 2.3M customer accounts to modernized system
- Achieved 96.4% code similarity matching accuracy using deep learning models
- Identified and resolved 847 security vulnerabilities through ML-guided fuzzing
- Implemented comprehensive CI/CD pipeline with automated testing
- Reduced system complexity by 73% through intelligent refactoring
Business Results
- Zero-downtime migration completed over 6-month period
- ROI of 340% within first year of implementation
- Customer satisfaction improved from 6.2/10 to 8.9/10
- Enabled 12 new digital banking features previously impossible
- Reduced technical debt by $4.1M annually
"The AI-driven approach to our legacy modernization exceeded all expectations. The precision of the binary analysis and the quality of the automated refactoring enabled us to transform our 25-year-old system with zero customer disruption."
Michael Chen
CTO, SecureBank Financial Corporation
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