The CA Gen Deadlock: Automating Screen Extraction for Insurance Mainframe Transformation

Mainframe systems are the silent engines of the insurance industry, yet they have become the single greatest bottleneck to digital agility. For organizations running CA Gen (formerly COOL:Gen), the challenge isn't just the age of the hardware; it’s the layers of proprietary abstraction that make extracting business logic and UI definitions feel like digital archaeology. With a global technical debt mountain reaching $3.6 trillion, insurance carriers can no longer afford the "rip and replace" cycle that historically leads to disaster.

According to Replay’s analysis, 70% of legacy rewrites fail or significantly exceed their timelines because teams underestimate the complexity of the "as-is" state. In the context of an insurance carrier, where a single policy administration system might have 4,000 unique screens, the manual effort required for documentation is staggering.

TL;DR: Manual screen extraction insurance mainframe projects typically require 40 hours of engineering and design effort per screen. Replay’s Visual Reverse Engineering platform reduces this to 4 hours by converting video recordings of user workflows into documented React code and Design Systems. This shifts the modernization timeline from years to weeks, achieving a 70% average time savings for enterprise insurance transformations.

The Complexity of Screen Extraction for Insurance Mainframe Workflows#

The insurance sector relies on CA Gen for its ability to manage complex, rule-heavy environments like claims processing and actuarial calculations. However, these systems were built in an era where the user interface was an afterthought. Extracting these screens for a modern web-based front end involves more than just copying text fields; it requires capturing the intent, the validation logic, and the state transitions inherent in the mainframe terminal.

Video-to-code is the process of recording a subject matter expert (SME) navigating a legacy application and using AI-driven visual analysis to generate functional, styled, and documented front-end code.

For most insurance firms, the documentation is the first casualty of time. Industry experts recommend a "documentation-first" approach to migration, yet 67% of legacy systems lack any form of up-to-date technical documentation. When you attempt a screen extraction insurance mainframe project manually, you aren't just building a new UI; you are reverse-engineering thirty years of undocumented business rules hidden behind green-screen shortcuts.

Modernizing Legacy UI requires a shift from manual mapping to automated discovery. By using Replay, architects can record real-world workflows—like a claims adjuster processing a complex multi-vehicle accident—and automatically generate the corresponding React components and state machines.

Why Traditional Screen Extraction Insurance Mainframe Projects Fail#

The traditional approach to mainframe transformation involves "Screen Scraping" or "Terminal Emulation." These methods are brittle. They rely on fixed coordinate systems (Row 10, Column 15) that break the moment a field is moved or a new data point is added. Furthermore, they do nothing to solve the underlying problem: the code is still legacy COBOL or CA Gen logic wrapped in a slightly prettier web shell.

An average enterprise rewrite timeline is 18 months. In that time, the business requirements have shifted, and the "modern" stack chosen at the start of the project is already maturing.

Comparison: Manual Extraction vs. Replay Automation#

The economic reality of the screen extraction insurance mainframe process is that manual efforts cannot scale. If an insurance platform has 500 critical screens, a manual approach requires 20,000 man-hours. At enterprise billing rates, that is a multi-million dollar gamble. Replay reduces that same scope to 2,000 hours, allowing teams to focus on innovation rather than transcription.

Technical Implementation: Mapping CA Gen to React#

When we look at CA Gen screens, we are looking at structured "Views" and "Action Blocks." In a modern architecture, these map to React components and API hooks. The challenge is that CA Gen often bundles UI state with business logic in a way that is difficult to decouple.

Industry experts recommend using an intermediary "Blueprint" phase. Blueprints are the structural definitions of a legacy screen, including field types, validation patterns, and layout constraints, extracted directly from visual data.

Below is a conceptual example of how a legacy insurance "Policy Inquiry" screen might look in its raw extracted state versus the React component generated by Replay.

Legacy Data Structure (Conceptual)#

typescript
// The raw output of a traditional screen scrape
interface LegacyPolicyScreen {
  scr_id: "POL-001";
  fields: [
    { row: 5, col: 10, label: "POLICY_NUM", value: "A123-456", length: 8 },
    { row: 6, col: 10, label: "INSURED_NAME", value: "JOHN DOE", length: 30 },
    { row: 7, col: 10, label: "EFF_DATE", value: "20231001", length: 8 }
  ];
  function_keys: {
    F3: "EXIT",
    F5: "UPDATE",
    F12: "CANCEL"
  };
}

Replay-Generated React Component#

tsx
import React from 'react';
import { usePolicyData } from '../hooks/usePolicyData';
import { TextField, DatePicker, Button, Card } from '@replay-ds/insurance-library';

/**
 * PolicyInquiry Component
 * Automatically generated from Workflow: "Standard Policy Review"
 * Source: CA Gen Mainframe Screen POL-001
 */
export const PolicyInquiry: React.FC<{ policyId: string }> = ({ policyId }) => {
  const { data, loading, updatePolicy } = usePolicyData(policyId);

  if (loading) return <SkeletonLoader />;

  return (
    <Card title="Policy Information">
      <div className="grid grid-cols-2 gap-4">
        <TextField 
          label="Policy Number" 
          value={data.policyNumber} 
          readOnly 
        />
        <TextField 
          label="Insured Name" 
          value={data.insuredName} 
          onChange={(val) => updatePolicy({ insuredName: val })}
        />
        <DatePicker 
          label="Effective Date" 
          value={new Date(data.effectiveDate)} 
        />
      </div>
      <div className="flex justify-end gap-2 mt-6">
        <Button variant="secondary">Cancel</Button>
        <Button variant="primary" onClick={updatePolicy}>Update Policy</Button>
      </div>
    </Card>
  );
};

By converting the screen extraction insurance mainframe output into a standardized Design System (Library), Replay ensures that every generated screen follows the organization's modern brand guidelines while maintaining the functional integrity of the original system.

The Role of "Flows" in Insurance Modernization#

In the insurance world, a screen never exists in isolation. A "New Business Application" might span twelve different screens, three sub-menus, and a complex series of "if-then" prompts. Traditional extraction misses the connective tissue between these screens.

Replay introduces the concept of Flows. By recording the entire sequence, the platform captures the architectural relationship between screens. It understands that "Screen A" submits data that populates "Screen B." This visual reverse engineering approach allows architects to see the entire application map, identifying redundant screens and streamlining the user experience.

According to Replay’s analysis, insurance carriers often find that 30% of their legacy screens are either redundant or can be consolidated into a single modern dashboard. This consolidation is only possible when you have a high-fidelity map of the existing workflows.

Mainframe to React Migration is not just about moving pixels; it’s about refactoring the user’s journey through the data.

Security and Compliance in Regulated Environments#

For Financial Services, Healthcare, and Government sectors, the "where" and "how" of data processing are non-negotiable. Screen extraction in an insurance mainframe environment often involves sensitive PII (Personally Identifiable Information) or PHI (Protected Health Information).

Replay is built for these high-stakes environments. The platform is SOC2 compliant and HIPAA-ready. For organizations with strict data sovereignty requirements, Replay offers an On-Premise deployment model. This ensures that the video recordings and the generated code never leave the secure corporate network, satisfying the most stringent CISO requirements.

Furthermore, the AI Automation Suite within Replay allows for the masking of sensitive data during the recording process. This means SMEs can record real workflows using production-like data without exposing actual customer secrets to the transformation pipeline.

Implementation Strategy: The Replay Workflow#

To successfully execute a screen extraction insurance mainframe project, we recommend a four-phase approach:

1. •Capture (Flows): SMEs record their daily tasks using the Replay recorder. This captures the visual state, user interactions, and data transitions.
2. •Analyze (Blueprints): Replay’s AI analyzes the recordings to identify common patterns, UI components, and data structures.
3. •Standardize (Library): The identified components are mapped to a React-based Design System. This ensures that the "Claims Search" screen looks and behaves exactly like the "Policy Update" screen.
4. •Generate (Code): The platform generates documented, production-ready React code that developers can then integrate with modern APIs or middleware layers.

This methodology eliminates the "blank page" problem that stalls most modernization efforts. Instead of starting from scratch, developers start with a 90% complete front end that is functionally identical to the legacy system but architecturally modern.

The Economic Impact of Visual Reverse Engineering#

The math of manual modernization is increasingly difficult to justify. With a $3.6 trillion global technical debt, the "manual" way is the path to obsolescence.

Consider a mid-sized insurance carrier with 1,000 legacy screens.

• •Manual Cost: 1,000 screens * 40 hours = 40,000 hours. At $150/hr, that’s $6,000,000.
• •Replay Cost: 1,000 screens * 4 hours = 4,000 hours. At the same rate, that’s $600,000.

This 90% reduction in labor cost allows the organization to reallocate budget toward the "Future State"—building the AI-driven features and customer-facing portals that actually drive revenue, rather than just keeping the lights on.

Frequently Asked Questions#

How does screen extraction insurance mainframe handle complex CA Gen action blocks?#

Replay focuses on the visual and behavioral output of the action blocks. While it doesn't "read" the CA Gen model directly, it captures the resulting UI state and data requirements. This allows developers to see exactly what data the action block expects and returns, making it significantly easier to write modern API equivalents.

Can Replay handle "Green Screen" terminal emulators?#

Yes. Replay is agnostic to the underlying technology. Whether the insurance system is a 3270 terminal emulator, a PowerBuilder application, or a CA Gen web-wrapped interface, the platform uses visual reverse engineering to extract the components and workflows.

Is the generated React code maintainable?#

Absolutely. Unlike "black box" migration tools that output spaghetti code, Replay generates clean, documented TypeScript and React code that follows modern best practices. The code is structured around your own Design System, making it indistinguishable from code written by a senior front-end engineer.

How does this approach impact the 18-month average enterprise rewrite timeline?#

By automating the discovery and UI generation phases, Replay typically compresses the initial 12 months of "as-is" analysis and front-end development into 2-4 weeks. This allows the overall project to finish in a fraction of the time, often moving from a 2-year roadmap to a 6-month delivery.

What industries besides Insurance benefit from this?#

While highly effective for the screen extraction insurance mainframe use case, Replay is widely used in Financial Services, Healthcare, Government, Manufacturing, and Telecom—any industry where legacy systems are deeply embedded and documentation is sparse.

Conclusion#

The era of the multi-year, high-risk mainframe rewrite is coming to an end. By leveraging visual reverse engineering, insurance carriers can finally break free from the constraints of CA Gen and other legacy frameworks. The goal is no longer just to "get off the mainframe," but to do so in a way that preserves business logic, empowers developers, and provides a superior user experience.

With Replay, the process of screen extraction insurance mainframe becomes a predictable, automated, and highly efficient pipeline. You can move from a legacy terminal to a modern, documented React library in weeks, not years.

Ready to modernize without rewriting? Book a pilot with Replay