AI-Driven QA Optimization Accelerates Insurance Releases by 30–56%

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Client Context

A large enterprise insurance provider operates a complex digital ecosystem spanning Policy Issuance, Claims, Underwriting, Payments, and Renewals, with frequent regulatory updates and large-scale releases.

Crestech was engaged for its insurance QA expertise and AI-led automation optimization capabilities to reduce automation cycle time, improve execution stability, and enable predictable releases at scale.

The Challenge

As the insurance platform evolved, existing automation began limiting speed and scalability.

Redundant Automation Scripts
Overlapping workflows across policy and claims journeys resulted in high maintenance effort during regulatory and product changes.

Long Execution Cycles
End-to-end regression for policy issuance and claims took 9–10 hours, delaying release validation.

High Manual Dependency
Critical insurance validations—premium calculations, endorsements, and claim payment flows—required frequent human intervention.

Complex, Unstable Environments
Automation spanned multiple integrations including Core PAS, Payment Gateway, CRM, and Data Lake, leading to flaky runs and reduced confidence.

Business Impact:
These issues slowed deployments, increased QA effort, and reduced the overall value of automation.

Crestech’s AI-Driven Optimization Approach

Crestech implemented a continuous QA optimization model, combining AI-assisted code intelligence, GenAI utilities, and framework restructuring.

1. Centralized & Intelligent Codebase

Consolidated all automation changes into a single main branch
AI-assisted PR reviews identified redundant logic and recommended optimized patterns

Impact: Reduced duplication and improved maintainability.

2. Optimized Automation Framework

Streamlined framework structure to eliminate unnecessary execution overhead
Improved execution efficiency across policy and claims workflows

Impact: Faster and more reliable test runs.

3. Script Simplification Using GenAI

Removed overlapping flows across Policies, Quotes, Renewals, and Claims
Introduced reusable components for underwriting rules, premium calculations, and compliance validations

Impact: Fewer scripts and faster updates during regulatory changes.

4. Parallel Execution at Scale

Enabled parallel execution across multiple policy types and product lines
Reduced dependency on serialized runs and manual triggers

Impact: Accelerated feedback without compromising stability.

5. Reduced Manual Effort Through Automation Assistants

Automated dynamic test data creation for policy issuance, endorsements, and product rate changes
Enabled QA teams to focus on risk-based testing and early defect detection

Execution Summary

Optimization Area	Outcome
Framework Optimization	Faster execution and quicker feedback
Script Simplification	Easier maintenance and faster updates
Parallel Execution	Accelerated release validation
Manual Effort Reduction	Greater focus on high-risk testing

Test Execution Time: Before vs After

Execution Type	Total Test Cases	Before	After	Improvement
Daily Run (Policy Ops)	154	3 hrs 25 mins	1 hr 30 mins	~56% faster
Full Regression	4,621	9 hrs 30 mins	6 hrs 30 mins	~32% faster

Business Impact

Accelerated Releases Faster and more predictable insurance release cycles.
Improved QA Efficiency Reduced repetitive work allowed teams to focus on complex insurance validations.
Higher Reliability AI-optimized scripts delivered stable execution across environments.
Strategic QA Focus Increased focus on regulatory validations, rate changes, product rules, and UAT support.

Conclusion

By combining AI-assisted automation intelligence, GenAI utilities, and a streamlined execution framework, Crestech transformed QA automation from a bottleneck into a release accelerator.

Results achieved: