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Taking Response Times to a New Level

How a leading global specialist insurer implemented Moody’s RMS Location Intelligence API in their underwriting process to increase their quote-to-bind ratio through consistent delivery of market-beating quote response times

New Level
Customer Story Specialist Insurer

Quotes generated under 3 seconds every time

Improved customer experience to win more business

Decreased operational costs by shifting from on-prem to SaaS

Key Takeaways

  • Quotes generated under 3 seconds every time
  • Improved quote-to-bind ratio
  • Consistent data and analytics across all underwriting and portfolio management stages
  • Decreased operational costs by shifting from on-prem to SaaS
  • Seamless integration into existing workflows
  • Extend quoting and bind capabilities to wider network
  • Improved customer experience throughout the underwriting process

The Challenge - To Ensure a Consistent Rapid Quote Response Time

A leading international specialist insurer wanted to boost its ability to deliver almost instantaneous quotes across its U.S. property portfolio for hurricane, flood, and earthquake-related perils. The company’s existing on-premises infrastructure could not deliver loss cost data with the speed or consistency of response times required for their quoting process.

A performance assessment had shown a direct correlation between speed of response and quote-to-bind conversion rates. A delay of only a few seconds reduces their success rate by over 90 percent. Therefore, it was business critical to integrate the data architecture necessary to achieve the required speed to quote without compromising on quality.

As a digital pioneer, the company also required that any new data solution was SaaS-based, offering the speed of performance enabled by cloud technology while removing the need to maintain slower, more resource intensive, on-premises infrastructure.

The Solution – Location Intelligence API

The Moody's RMS Location Intelligence API met every business objective. The cloud-based service provides real-time, location-level hazard, risk and loss metrics that support underwriting workflows, facilitating improved risk selection, enhanced risk screening and more refined risk pricing.

Central to the insurer’s demands, Location Intelligence API enables straight-through-processing with a simple API call providing loss cost data within seconds. Moody’s RMS load testing demonstrated that sub-3-second response times could be achieved consistently, even for the most complex composite data calls.

The Location Intelligence API leverages our market-leading models based upon the best available catastrophe science. The service delivers a consistent, single view of risk across the organization based on a common set of analytics that span the full risk transfer process, from individual risk through to portfolio.

The Outcome – Consistent Quotes in an Instant Every Time

The successful integration of Location Intelligence API has enabled the insurer to meet its market-leading quote response times, which in turn has increased its overall quote-to-bind ratio and improved the quantity and quality of underwritten policies.

The company has been able to introduce live pricing inputs and organization-specific risk screening metrics directly into its underwriting process, ensuring a consistent view of risk. Integrated seamlessly into the business workflow, the API connects all points in the chain from front-end risk assessment to top-level portfolio management, ensuring uniform data and analytics at every stage.

The pioneering company also employs this new infrastructure within its delegated authority network in the U.S. Agents are provided with access to its quote and bind facility via a portal ensuring not only speed of response, but also that its risk appetite criteria are applied across every risk underwritten.

In addition, as a cloud-based API, the company was able to embed Location Intelligence API seamlessly into its infrastructure within three weeks. Switching off its on-premises technology and transitioning to SaaS not only improved data performance, but also removed the associated costs and resource demands.

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