How Scry AI Reduced Legacy Code by 96% for Insurance Illustration Systems

Customer Overview

A leading life insurance provider relied on a legacy COBOL-based system to generate policy illustrations, run complex insurance calculations, and support critical customer-facing processes.

As business needs, regulatory requirements, and product innovation demands increased, the system became harder to maintain and scale. The insurer needed a modernization strategy that preserved decades of embedded business logic while reducing dependency on aging technology and enabling future digital capabilities.

The Challenge

Life insurance illustration systems depend on complex business rules built over decades. These rules govern premium calculations, policy values, riders, tax treatments, loans, state-specific requirements, and other insurance-specific scenarios.

In this case, much of the logic was embedded within millions of lines of legacy COBOL code, making it difficult to locate, interpret, and update. Traditional modernization would have required extensive manual reverse engineering, increasing cost, risk, and implementation time.

The organization faced several challenges:

Limited visibility into business logic within legacy COBOL applications

High effort required to document decades of accumulated rules

Difficulty supporting new product and business requirements

Dependence on aging technology that restricted innovation

Risk of affecting regulatory and illustration accuracy during modernization

High cost and risk associated with full system replacement

The Solution

Scry AI implemented its AI Based Illustrations solution to modernize the legacy insurance illustration system without treating the effort as a simple code conversion project. The solution focused on extracting business logic, reducing technical debt, and rebuilding the platform on a more scalable foundation.

Automated Legacy Code Analysis

Instead of manually reviewing millions of lines of COBOL code, the solution automatically analysed the existing application and mapped relationships across system components.

Business Rule Extraction

Embedded business rules, calculation logic, and decision pathways were extracted and converted into structured knowledge. This made the logic easier to review, validate, and reuse in the modernized environment.

Data Flow and Decision Mapping

Advanced data flow mapping and automated rule discovery helped reconstruct complex insurance calculations and workflows. Decision trees exposed hidden logic that had been difficult to document or maintain.

Modern Architecture Enablement

The extracted intelligence became the foundation for rebuilding the platform within a modern architecture. This supported complex insurance calculations, natural language generation, and future business or regulatory changes.

Results at a Glance

Metric	Outcome
Code Reduction	96% reduction from 2 million lines of COBOL to 75,000 lines of modernized code
Illustration Accuracy	100% accuracy with regenerated illustrations matching legacy outputs
Project Timeline	Completed in just 6 months
Operational Costs	50% reduction in modernization effort and associated costs
Business Rule Discovery	Automated extraction and documentation of embedded logic
System Maintainability	Improved agility and ease of future enhancements
Technology Modernization	Transition from legacy architecture to a scalable modern platform
Innovation Readiness	Enabled support for advanced digital capabilities and future growth

Transforming Legacy Insurance Systems into Future-Ready Digital Platforms

Scry AI’s AI Based Illustrations solution helped the insurer modernize its legacy environment while preserving decades of embedded business logic. By automating rule discovery, reducing system complexity, and enabling modern illustration capabilities, the organization built a scalable foundation for innovation, compliance, and long-term growth.

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