From Months to Minutes: Transforming Howden's FinTechOS Testing Through Automation

PISR: Problem, Impact, Solution, Result

Problem: Howden's FinTechOS B2B Insurance Broker platform was trapped in a cycle of extensive manual testing that consumed 42 hours per release across UI workflows, product configurations, and integration testing. With 56% of delivery time spent on manual regression testing and releases taking weeks to months, the platform couldn't keep pace with business requirements or competitive pressures in the insurance technology market.
Business Impact: The manual testing bottleneck meant only 2 releases every 6 months, totalling 13.5 weeks of manual regression effort per period. This created a massive constraint on business agility, prevented rapid response to market opportunities, and consumed enormous QA resources on repetitive tasks rather than exploratory testing that could uncover critical issues.
Our Solution: ClearRoute conducted a comprehensive Route To Live assessment and deployed a 4-month transformation programme focusing on test automation, DevOps practices, and team restructuring. We implemented comprehensive UI automation with Cypress, API testing with REST Assured, integrated testing into Azure DevOps pipelines, and established Test-First development practices with proper environment strategy and monitoring.
Tangible Result: We achieved a 94% reduction in UI workflow testing (from 26 hours to 90 minutes), 99% reduction in product logic testing (from 6 hours to 5 minutes), and 90% reduction in integration testing (from 10 hours to 60 minutes). The transformation enabled fortnightly releases, freed QA teams for high-value exploratory testing, and established automated feedback loops that catch regressions before production.

The Challenge

Business & Client Context

Primary Business Goal: Accelerate delivery of new insurance products and features through Howden's FinTechOS platform to create competitive advantage and better customer experience whilst reducing manual testing overhead.
Pressures: Insurance market competitiveness required faster product launches, regulatory changes demanded rapid platform updates, and the existing manual testing approach was consuming over half of all delivery capacity with poor release predictability.
Technology Maturity: Sophisticated FinTechOS platform deployment with Octopus Deploy automation, but zero test automation, siloed development environments, and trunk-based development without proper testing gates.

Current State Assessment: Key Pain Points

Massive Manual Testing Bottleneck: 42 total hours of manual testing per release including 26 hours of UI workflow regression, 6 hours of product-specific logic validation, and 10 hours of integration testing with downstream legacy systems.
Extended Release Cycles: Business requirements analysis taking 7 weeks, followed by development phases where 56% of time was consumed by manual regression testing, resulting in only 2 releases every 6 months with unpredictable delivery timescales.
Environment and Team Silos: Development and testing split across two separate cloud environments (SoftElligence and Howden), separate backlog management tools, and disconnected teams that created handover points and communication breakdowns throughout the delivery process.

Baseline Metrics (Where Available)

Metric Category	Baseline	Notes
UI Workflow Testing	26 hours manual	Quote workflows across multiple products
Product Logic Testing	6 hours manual	Rates, referrals, endorsements validation
Integration Testing	10 hours manual	Downstream legacy system verification
Release Frequency	Every 6 months	Only 2 releases per year
Manual Testing Proportion	56% of delivery time	13.5 weeks per 6-month period

Solution Overview

Engagement Strategy & Phases

Phase 1: Route To Live Assessment (Months 1-2): Comprehensive analysis of current development practices, testing approaches, infrastructure setup, and DevOps maturity. Identified critical bottlenecks and designed transformation roadmap focusing on test automation and process improvements.

Phase 2: Test Automation Foundation (Months 2-3): Implemented comprehensive test automation suite using Cypress for UI testing, REST Assured for API testing, and established Test-First development practices with feature branch strategy and automated pipeline integration.

Phase 3: Infrastructure & DevOps Integration (Months 3-4): Integrated automation into Azure DevOps pipelines, established proper environment strategy with monitoring and alerting, implemented visual regression testing, and delivered team up-skilling and knowledge transfer.

Architectural Overview

Before State: Manual Testing Silos

After State: Automated Testing Pipeline

QCE Disciplines Applied

Quality Engineering: Implemented comprehensive test automation pyramid with Cypress for UI, REST Assured for APIs, and proper test data management. Established Test-First development practices that shifted quality left and embedded testing throughout the development lifecycle.
Developer Experience: Transformed development workflow with feature branching strategy, automated testing feedback in CI pipelines, and local environment setup for QA engineers. Enabled faster, more confident development cycles with immediate feedback on code quality.
Platform Engineering: Integrated automation suite into Azure DevOps infrastructure with daily scheduled execution, implemented Azure Monitor and Application Insights for observability, and established proper environment strategy with automated deployment validation.

The Results: Measurable & Stakeholder-Centric Impact

Headline Success Metrics

Metric	Before Engagement	After Engagement	Improvement
UI Workflow Testing	26 hours manual	90 minutes automated	94% reduction
Product Logic Testing	6 hours manual	5 minutes automated	99% reduction
Integration Testing	10 hours manual	60 minutes automated	90% reduction
Release Frequency	Every 6 months	Fortnightly releases	12x improvement
Total Testing Time	42 hours manual	155 minutes automated	94% reduction

Value Delivered by Stakeholder

For the Programme Manager:
- Transformed release predictability from unpredictable 6-month cycles to reliable fortnightly releases, enabling better business planning and faster response to market opportunities.
- Reduced testing overhead from 56% of delivery capacity to minimal automated execution, freeing significant resources for feature development and business value creation.
- Established measurable delivery metrics through Azure DevOps dashboards with story point velocity tracking and automated release forecasting capabilities.
For QA Engineers and Testing Teams:
- Eliminated 42 hours of repetitive manual regression testing per release, allowing focus on high-value exploratory testing and quality analysis rather than repetitive script execution.
- Gained modern test automation skills through hands-on training in Cypress, REST Assured, and Test-First development practices that enhanced career development and job satisfaction.
- Achieved immediate feedback on code quality through automated pipeline integration, catching regressions within hours rather than weeks into the testing cycle.
For Development Teams:
- Implemented Test-First development approach that improved code quality and reduced defect rates by establishing clear acceptance criteria and automated validation before merge.
- Established feature branch strategy with automated testing gates that prevented broken code from reaching shared environments, reducing development friction and debugging time.
- Gained confidence in releases through comprehensive automated coverage that validated both functional requirements and integration points with downstream systems.

Key Technical Achievements

Comprehensive Test Automation: Successfully implemented full test pyramid with UI automation using Cypress, API testing with REST Assured, and integration testing for downstream legacy systems, all integrated into daily Azure DevOps pipeline execution.
Visual Regression Testing: Implemented automated validation of policy document generation, ensuring consistent document quality without manual verification effort.
Infrastructure Monitoring: Established Azure Monitor and Application Insights with Teams integration for 24/7 alerting, providing proactive visibility into platform health and performance issues.

Lessons, Patterns & Future State

What Worked Well

Test-First Development Adoption: The collaborative approach to defining acceptance tests with business representatives, QA, and developers during planning ensured comprehensive coverage whilst keeping test execution times minimal.
Comprehensive Automation Suite: Building automation for UI workflows, product logic, and integration testing simultaneously provided complete coverage and eliminated the manual testing bottleneck across all critical areas.
Azure DevOps Integration: Leveraging the client's existing Azure DevOps infrastructure for automated test execution ensured seamless adoption and ongoing sustainability without additional tooling overhead.

Challenges Overcome

Complex Product Configuration Testing: Collaborating with development teams to expose internal rates and referrals mechanisms enabled high-throughput automation of complex insurance product calculations that previously required days of manual validation.
Environment Strategy Transformation: Successfully unified testing across previously siloed SoftElligence and Howden environments, establishing consistent CI environment for automated testing without manual contamination.
Team Skill Development: Overcame initial resistance to Test-First practices through hands-on training and knowledge transfer, establishing sustainable automation capabilities within the existing team structure.

What We'd Do Differently

Earlier Monitoring Implementation: Azure Monitor and Application Insights setup should have been prioritised earlier in the engagement to provide baseline performance data and catch environment issues sooner.
Gradual Automation Introduction: A more phased approach to automation implementation might have reduced initial change management overhead, though the comprehensive approach delivered faster overall transformation.
Cross-Environment Collaboration: More upfront investment in aligning SoftElligence and Howden team practices could have accelerated the unified development approach adoption.

Key Takeaway for Similar Engagements

When transforming testing practices in complex insurance technology platforms, focus on comprehensive automation across all testing layers simultaneously rather than incremental adoption. The business impact of eliminating manual testing bottlenecks justifies the upfront investment in building complete automation coverage.

Replicable Assets Created

FinTechOS Test Automation Framework: Cypress and REST Assured testing patterns specifically designed for insurance product configuration and workflow validation
Test-First Development Process: Proven methodology for collaborative test definition and feature branch development with automated validation gates
Azure DevOps Pipeline Templates: Reusable CI/CD patterns for insurance technology platforms with comprehensive testing integration
Insurance Product Testing Patterns: Reusable approaches for automating complex insurance calculation validation and policy document verification

Client's Future State

The automation framework established the foundation for Howden's ongoing FinTechOS platform evolution. With fortnightly release capability and comprehensive automated testing coverage, Howden can rapidly respond to insurance market changes, regulatory requirements, and customer demands. The Test-First development practices and Azure DevOps integration provide sustainable improvement in delivery predictability and quality assurance.

Internal Learning: Insurance Technology Testing Complexity

This engagement demonstrates that complex insurance product configuration testing can be successfully automated when development teams collaborate to expose internal calculation mechanisms. The key insight is that achieving 99% reduction in product logic testing requires close partnership with developers to create testable interfaces, rather than attempting to automate purely through UI interactions. Future insurance technology engagements should prioritise API-level testing capabilities for complex business logic validation.