How One Insurer Transformed Its Business With Digital

This article outlines a composite case study, drawing on common patterns and challenges we've observed across multiple engagements, rather than detailing a specific client project. Our hypothetical client, "Evergreen Insurance," was a regional carrier based in the US Midwest, specializing in commercial property and casualty policies for small to mid-sized businesses. They had been in operation for over 60 years and served approximately 40,000 policyholders.

The Starting Point

Evergreen Insurance faced a familiar set of challenges rooted in decades of incremental technology adoption. Their core policy administration system, built on COBOL in the early 1990s, was stable but lacked modern API capabilities. Underwriters relied on a mix of manual data entry into spreadsheets, email communication for approvals, and physical file reviews. This contributed to an average policy issuance time of 14 business days for new commercial P&C policies, often longer for complex cases. Customer service representatives navigated five distinct applications to answer a single policyholder inquiry, leading to an average call handle time of 8 minutes and 30 seconds.

Their claims process was similarly fragmented. Adjusters manually uploaded photos and reports from the field, often waiting days for these to be reconciled with internal systems. This created a bottleneck that pushed the average claims resolution time to 35 days, impacting customer satisfaction and increasing operational overhead. Furthermore, a significant portion of their IT budget, approximately 60%, was dedicated to maintaining these legacy systems, leaving little room for innovation. Employee morale was declining, with junior staff struggling to learn outdated interfaces and senior staff nearing retirement, threatening a loss of institutional knowledge.

What They Shipped

Over an 18-month engagement, Evergreen Insurance, in partnership with Hostreck, executed a phased digital transformation focused on core business processes.

1. Underwriting Workflow Automation: We designed and implemented a new underwriting workbench built on a microservices architecture, integrating with external data sources like property intelligence APIs (e.g., CoreLogic, Verisk) and internal legacy policy data via a new integration layer (Apache Kafka). This system automated risk assessment for 70% of new business applications, flagging only complex cases for manual review.
2. Customer Self-Service Portal: A new customer portal was launched, allowing policyholders to view policy documents, submit basic claims, update contact information, and make payments online. This portal was built using React for the frontend and a secure API gateway that connected to relevant backend systems.
3. Mobile Claims Companion App: Field adjusters received a custom-built mobile application (iOS and Android) that enabled real-time photo uploads, GPS tagging, voice-to-text damage descriptions, and offline data capture. This app integrated directly with the claims management system, streamlining the data collection process.
4. Data Lake and Analytics Platform: A centralized data lake (AWS S3) was established, consolidating data from the legacy policy system, claims, underwriting, and the new customer portal. This fed into a business intelligence platform (Tableau) for real-time reporting on key operational metrics and risk exposure.
5. Legacy System API Layer: Instead of a full rip-and-replace, a strategic API layer was built around the existing COBOL policy administration system using an enterprise service bus (MuleSoft). This allowed new applications to interact with core policy data without directly modifying the legacy code, preserving stability while enabling modernization.

The Numbers, 6 Months In

Six months post-rollout of the primary features, Evergreen Insurance saw tangible improvements across their operations.

• Policy Issuance Time: The average time to issue a new commercial P&C policy dropped from 14 business days to 5 business days, a 64% reduction. Complex policies, while still requiring manual review, saw their processing time reduced by 30% due to better data availability.
• Claims Resolution Time: Average claims resolution time decreased from 35 days to 22 days, a 37% improvement. This was largely attributed to faster data capture and improved visibility for claims managers.
• Customer Service Efficiency: Average call handle time for customer service inquiries decreased from 8 minutes 30 seconds to 5 minutes 15 seconds, a 38% reduction. This was driven by the consolidated view provided by the new systems and the deflection of routine inquiries to the self-service portal.
• Operational Cost Savings: Early projections indicated a 15% reduction in IT maintenance costs over the next 12 months, shifting resources towards innovation and growth initiatives.

What We'd Do Differently

Looking back, two key areas stand out where Evergreen Insurance could have optimized their approach.

First, while the API layer around the COBOL system was effective, a more aggressive strategy for migrating specific, high-transaction modules out of the legacy system earlier in the roadmap would have yielded greater agility. The initial focus was on minimal disruption, but a deeper dive into module-by-module replacement could have accelerated certain benefits, particularly around scalability.

Second, the internal change management program, while present, could have been more robust and proactive. While new systems were technically sound, some long-tenured employees struggled to adapt to new workflows and digital tools. Earlier and more intensive training, coupled with dedicated "super-users" within each department, might have smoothed the transition and boosted user adoption rates faster.

Other insurance teams can learn from Evergreen's experience that phased modernization, focusing on high-impact areas like underwriting and claims, can deliver significant returns without the prohibitively high risk of a full-scale legacy system replacement. The strategic use of API layers to unlock legacy data is a powerful accelerator for new digital products, but a clear long-term vision for legacy disentanglement is still crucial.