facebook
Home > Case study > Re-Engineering Quote Processing With Data Driven Modernization 

How Quinnox Helped a Leading Insurer to Re-Engineer Insurance Quote Processing Through Data Driven Modernization

Qinfinite-Chaos-Engineering

Client Overview

A leading U.S.-based insurance provider serving millions of customers operates in a high-volume group insurance environment, processing up to 15 quotes per second during peak hours. The quoting system supports complex scenarios with census data ranging from 100 to 50,000 lives and spans over 15 insurance product types and nearly 500 products, making performance, scalability, and accuracy mission-critical. 

Business & Technology Challenges

  • The organization faced significant challenges impacting technical landscape, operational efficiency and scalability: 
  • Performance Bottlenecks: As quote volume and complexity increased -especially for large census quotes (10,000–50,000 lives), processing times rose sharply. Large quotes took over one hour in production, causing delays that affected customer service and business operations. 
  • Scalability Crisis: A schema change to the factors data tables (column additions) broke the in-memory caching mechanism, forcing fallback to direct SQL Server queries. The resulting spike in database load caused severe performance degradation and required immediate doubling of production servers to maintain business continuity, exposing architectural fragility. 
  • Sustainability Concerns: Leadership determined the existing architecture was not viable long term. With growth projections and the need to support operations over the next 10–20 years, comprehensive modernization was required to ensure scalability, reliability, and cost efficiency. 
  • Legacy Infrastructure: The batch rating system ran as a .NET Framework Windows service on on-premises Windows servers, limiting flexibility and scalability for modern cloud operations. 
  • Database Performance Issues: Factors data critical to quote processing resided in SQL Server across two tables with approximately 10 million records. Although largely static, this data was accessed repeatedly during each quote cycle, creating a performance bottleneck. 
  • Caching Failure: Schema changes invalidated the in-memory cache, forcing frequent SQL Server queries for factor data and significantly increasing database load and query latency. 
  • Scalability Limitations: Performance degraded as census size increased. Quotes with ~3,000 lives performed moderately, while quotes nearing 30,000 lives experienced severe slowdowns, exceeding one hour in processing time. The on-premises setup lacked dynamic scaling capabilities. 
  • Operational Constraints: Manual server scaling made it difficult to respond quickly to demand spikes or performance issues. The emergency infrastructure expansion required to mitigate the SQL Server spike was costly and underscored by the need for a more flexible, cloud-based model. 

Quinnox Solution and Approach

  • Quinnox implemented a phased modernization strategy to address performance and scalability challenges while minimizing risk and ensuring business continuity.  
  • The engagement began with a detailed assessment of the batch rating system, including quote volume and processing patterns, factors data access behavior, performance bottlenecks, infrastructure utilization, and data distribution models. This analysis identified that factors data access was predominantly read-heavy with infrequent writes, which directly informed the database modernization strategy. The solution was executed in two phases. 

Phase 1: Infrastructure Modernization 

  • The existing .NET Framework Windows service was lift-and-shifted from on-premises infrastructure to AWS EC2, with minimal application changes.  
  • AWS Auto Scaling was implemented to enable dynamic, demand-based scaling and eliminate manual server provisioning. 

Phase 2: Data Driven Modernization 

  • Factors data was migrated from Microsoft SQL Server to MongoDB to improve read performance.  
  • A data architecture of 175 MongoDB collections, one per factor type, was designed to optimize query efficiency, enforce factor type–based access, and support future data growth. 
  • Comprehensive indexing was applied across all collections to achieve sub-millisecond query performance.  
  • Technology Stack / Tools Used: Amazon Web Services (AWS) – Cloud platform, AWS EC2 instances, MongoDB (for factors data), Microsoft SQL Server (retained as source of truth, .NET Framework (Windows Service), AWS Auto Scaling.  
  • Implementation followed a phased rollout with validation at each stage, including performance parity checks, application code updates to query MongoDB, and thorough testing. A governance framework was established covering automated data synchronization, change management for future schema updates, continuous performance monitoring, and risk mitigation to ensure uninterrupted operations throughout the migration. 

Business & Technical Results Achieved

The data-driven modernization initiative delivered significant measurable improvements across performance, scalability, and operational efficiency. 

  • Faster quote turnaround, improving customer experience 
  • Cost optimization through auto-scaling and reduced infrastructure overhead 
  • Future-ready architecture supporting long-term growth with emergency scaling 
  • 50% faster processing for ~3,000-life quotes 
  • 90% faster processing for ~30,000-life quotes (from >1 hour to <6 minutes) 
  • Sub-millisecond MongoDB data retrieval 
  • Significant reduction in SQL Server load 
  • Fully automated, proactive scalability 

Quantifiable Impact

Metric Before After Improvement
Processing Time (3,000 lives) Baseline 50% faster 50% improvement
Processing Time (30,000 lives) >1 hour <6 minutes 90% improvement
Data Retrieval Time Variable (SQL Server dependent) Sub-millisecond Single-digit milliseconds
Infrastructure Scaling Manual (days) Automatic (minutes) Dynamic auto-scaling
SQL Server Load High (all queries) Reduced (source only) Significant reduction
Scalability Response Reactive (emergency doubling) Proactive (auto-scaling) Eliminated emergency scaling

Related Insights

Case study
Qinfinite

How Intelligent Twin Implementation for Middleware Integration Platform Helped Reduce Post-production Issues by 90%  

Discover how intelligent observability, automation & continuous learning led to 90% fewer post-production issues in middleware operations for a leading enterprise.

Read more
Case study
Customer Experience

Quinnox Helps a Leading Global Beverage Company Overcome Complex Integration Challenges to Enhance Customer Engagement and Streamline Business Processes 

A globally renowned soft drink company operating across nine countries, faced the aftermath of mergers and acquisitions, resulting in disparate systems and processes. The need for a sophisticated customer engagement platform (CEP) was critical to foster seamless interactions and drive revenue growth

Read more
Case study
Digital

Optimized IMS for America’s leading provider of retail automotive inventory software solutions

Our customer had developed an in-house Inventory Management System (IMS) that comprised of a complex and heavily customized code

Read more
Contact Us

Get in touch with Quinnox Inc to understand how we can accelerate success for you.