Case Studies

Insurance Broker: Standardizes and Automates to Enable Profitable Scale-Up

Large Insurance Broker
North America


The insurance brokerage ranked in the U.S. top 20. Its business was primarily concentrated in commercial P&C (60% of premiums) and employee benefits (30% of premiums). Historically, it had grown by acquiring firms when these became available and leaving the operations as-is.

The recently hired CEO struggled with a range of frustrating challenges. It’s conventional, “producer-centric” culture typical of almost all comparable brokerages was dominating every aspect of operations and stifling productivity growth. His challenge was to separate the producers from the mundane, easily standardized tasks, allow these to be automated and enable this rapidly expanding business to scale up profitably.

The CEO knew he had a tough job ahead to persuade the presidents of each division to standardize and automate their operations. Working in his favor, however, was an upcoming round of acquisitions that would increase volumes by 20% or more. Leadership needed to determine how they would handle this increased workload amidst generational turnover and a tight labor market. He contacted The Lab, having previously worked with together on a successful large-scale transformation engagement at an insurance carrier. 

Project Overview

Project Sponsor: President & CEO
Client: Top 20 U.S. Insurance Broker

Implementation Results – Wave 1 of 3
  • Employee capacity: Up 25%
  • Annual cost savings: $2.3M
  • Break-even point: 5 mos.
  • 12-month ROI: 4x

Project Approach

Phase I of the two-phased, brokerage-wide effort started with current state process mapping for the key work activities in both commercial P&C and employee benefits.

  • Prospecting and Sales
  • Quote-to-Bind
  • Policyholder Services – endorsements, cancellations, etc.
  • Renewals

Their current state operations were then benchmarked against The Lab’s proprietary industry best practice process models. The findings were not unexpected, but the quantification and documentation developed during Phase I increased the executive team’s sense of urgency.

Key findings:

  • Scalable capacity, company-wide – 19% to 32%
  • Process improvement opportunities – 90+ documented
  • Employee productivity variance – up to 4X for identical work
  • Revenue concentration – top 10% of clients generated over 80% of total revenue
  • Percentage of quote requests with missing information – over 90%

Finally, the Phase I findings and benefits were grouped into 12 modular work plans. These were further sequenced into three waves, each including 4 works plans, for implementation in Phase II.

Key Output
  • Current-state process maps
  • Future-state process design
  • Performance benchmarks
  • Benefits case and implementation work plan
  • Robotic process automation (RPA)
  • Process and data standards
  • Standard KPI definitions, goals
  • Power BI dashboards (automated)
    • Executive scorecard
    • Individual management reports

Implementation Work Plan Examples

1. Carrier Quote Generation (Commercial P&C)

The brokerage faced several challenges in its commercial lines quoting process for both new business and renewals. Over 90% of quote requests submitted by producers included incomplete or incorrect information. Data entry into carrier portals was manual and error prone.
Standardized documentation requirements were defined by coverage and carrier. Then, a digital intake form was launched to collect quote data and documents. Finally, automation (RPA) was deployed to transfer data from the newly launched quote form into various carrier portals. These new tools were first piloted for renewals – then refined and expanded to cover new business.

2. Policyholder Service (Commercial P&C, Employee Benefits)

The CEO targeted policyholder service for wave 1 implementation for two reasons: about half of the brokerage’s headcount was concentrated there – and, more importantly, any operational changes in the area would not directly impact the sales producers.
A gap test against industry best practices indicated that the agency lacked several service “basics,” including:

  • Tracking for inbound service requests, calls
  • Service level agreements (SLAs), goals
  • Customer tiers, or prioritized service levels

The Lab’s segmentation revealed that the top 10% of customers accounted for over 80% of revenue – yet each customer received the same level of service. Custom SLAs and service standards provided white glove service to the best customers.
Next, the highest volume service requests were identified for standardization and automation. Certificate requests, endorsements, and billing audits rose to the top of the list. New automated processes cut manual labor and improved tracking for those request types, including:

  • Digital intake forms to collect request data and track completion
  • Automation to move form data into the AMS and other locations
  • Standard, auto-triggered customer notifications

3. Management Reporting (Brokerage-Wide)

Management reports were created ad hoc by the finance team or individual area managers. No standards existed. Employee benefits and commercial P&C data were spread across two AMS’s, further complicating executive reporting. The CEO and CFO led a charge to become more data driven.

The Lab built a centralized data lake to combine data from multiple AMS’s, the CRM, and other sources into a “single source of truth.” Performance dashboards – built around 18 standard “Super KPIs” – were automated and deployed to executives and functional managers. Advanced reports were launched to bring visibility to profitability, risk, and more. For example, one report revealed that the top 12 of over 500 carriers accounted for more than half of revenue. This costly “long tail” of carriers was an ideal target for reduction.

Project Objectives
  • Increase scale to handle upcoming acquisitions
  • Build buy-in across division leads
  • Define future state process design
  • Free employees from manual “drudge” work
  • Standardize service delivery (without sacrificing quality)
  • Upgrade management reporting, visibility
Project Summary
  • Two-phased project
  • Phase I, analysis and design
    • 6-weeks
  • Phase II, implementation
    • 3 modules/waves, 20 weeks each
  • Self-funding benefits
  • No new technology required

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