Increased win rate and improved returns with an AI-Driven Quotation Engine

Removing the spreadsheet bottleneck in commercial insurance

The client faced a significant hurdle in their commercial underwriting division. Processing a single insurance quote required agents to gather data, reference massive tables, and run manual calculations across disconnected spreadsheets. This manual process took several days per quote. During this waiting period, prospective clients often took competing offers from faster insurance providers.

To solve this, we looked closely at how underwriters and agents collaborate. We found that the delay was not just in the calculation itself, but in the constant back and forth communication required to gather missing details. By identifying the minimum required data points for an accurate quote, we designed a digital workflow that eliminates unnecessary steps.

Embedding AI where it matters most

Instead of building a separate AI tool that agents would have to learn, we embedded predictive models directly into their daily workspace. The system pulls historical claims data and external risk factors automatically once an agent enters a business address and industry type. This change allowed the team to move away from manual data entry.

The AI engine calculates risk probabilities and suggests optimal pricing tiers instantly. This removes the guesswork from underwriting while keeping a human in the loop. Underwriters can still adjust the suggested rates within pre approved limits. This ensures the company maintains control over complex cases while automating the standard ones.

Faster turnarounds and better margins

The launch of the digital quotation engine changed how the sales team operates. Agents no longer hesitate to bid on complex commercial policies because they can deliver a professional, accurate proposal during their initial conversations with clients. They can now provide a level of service that was previously impossible.

By cutting response times from days to minutes, the client secured a higher volume of new business. At the same time, the automated risk engine helped eliminate underpriced policies. This protected the company's loss ratio and improved overall underwriting profitability by ensuring every quote is backed by data.