From COBOL to Python: A Practical Migration Playbook

Legacy COBOL systems still power critical infrastructure in banking and insurance. Migrating them requires surgical precision — zero downtime, zero data loss, and often zero tolerance from stakeholders. Here is the playbook I developed across multiple banking engagements.

Phase 1: Understand Before You Rewrite

Before writing a single line of Python, map every COBOL program, copybook, and JCL job. Automated analysis tools like CodeCarbon and custom AST parsers help build a dependency graph. You cannot migrate what you do not understand.

Phase 2: The Strangler Fig Pattern

Do not attempt a big-bang rewrite. Use the Strangler Fig pattern: route new traffic through a Python facade that delegates to COBOL for unmigrated functionality. Over time, replace each COBOL module behind the facade.

Phase 3: AI-Assisted Translation

Modern LLMs can translate COBOL to Python with 70-80% accuracy. The remaining 20% requires human expertise, particularly around packed decimal arithmetic, EBCDIC encoding, and VSAM file handling. AI accelerates the process but does not eliminate the need for deep COBOL knowledge.

Phase 4: Testing and Validation

Every migrated module needs byte-for-byte output comparison against the original COBOL program. We build automated test harnesses that run identical inputs through both systems and flag any discrepancies. This is non-negotiable in financial services.

Phase 5: Cutover Strategy

The final cutover uses a blue-green deployment with real-time data replication. Both systems run in parallel for a validation period. Rollback is always one click away. In our most recent banking migration, we achieved zero downtime and zero data discrepancies across 2.3 million daily transactions.

Results

The modernized Python system runs 10x faster, costs 60% less to maintain, and enables the bank to ship new features in days instead of months.