One of the top 10 carriers in the U.S. was struggling to keep up with submissions. They needed to find a way to shorten their underwriting process without sacrificing data accuracy, while minimizing their underserved submissions and keeping up with consumer expectations. Triaging and prioritizing submissions would allow them to focus on the businesses that they are likely to bind and weed out those that would just eat up an underwriter’s time. This insight would greatly improve submission-to-quote rates and contribute to generating more written premiums.
Accurate and complete data is paramount for establishing better pricing and ensuring lower losses. Many times, submissions are delivered incomplete. Manual data validation, using traditional underwriting research methods, is an extremely time-consuming process that leaves carriers vulnerable to omissions, human error, and premium leak. This follow-up research can be a mind-numbing experience and can lead to burnout of highly-trained underwriters, who are difficult to replace.
Agents had complained that the submission process was overly complicated, and each policy then required an average of four and a half hours for underwriting research and validation. Back-and-forth exchanges and delayed responses were contributing to poor agent and customer experiences. Additionally, legacy systems made it extremely difficult to enact new intake strategies, resulting in the carrier missing about 20% of submissions due to long processing times. And industry surveys indicate that the first carrier to quote in small commercial gets the business more than half of the time.
According to the Salesforce 2021 State of the Connected Customer report, 80% of customers say the experience a company provides is as important as its products or services, and 88% expect companies to accelerate their digital initiatives to accommodate expectations.
Planck showed the carrier how they could easily implement AI-based submission prioritization into their existing process. Upon form submission, an automated search builds a complete risk profile to validate the declared information, highlighting inaccuracies and supplementing omitted data. Planck’s proprietary search algorithms were built to understand and uncover business risk, and machine learning and modeling provide additional insights not available anywhere else–like percentage of revenue from liquor sales. Using filters determined by the carrier, submissions are then instantly categorized as 1.) approved, 2.) in need of specific underwriter intervention, or 3.) rejected—all based on real-time, aggregated risk data and carrier preferences. This process allows underwriters to focus on risks that are more likely to bind, while minimizing the amount of detective work and analysis required to validate unprofitable submissions.
The carrier noticed an immediate improvement in their intake process, with better processing speeds of relevant submissions. Submission-to-quote and conversion rates were shown to be significantly higher, and an additional $250K was immediately generated from a more efficient and data-based approach to underserved submissions.
Discover the truth behind any business
Improve your submission-to-quote turnover time
Reduce your loss ratios
Minimize your expense ratios
Improve agent collaboration
Streamline your underwriting operating model
See how Planck can improve your bottom line with better data, more relevant insights, and underwriting efficiencies.