Planck Resolution
Disrupting Commercial Insurance Underwriting

Insurance companies are one of the pioneers in advanced analytics, but most organizations have faced major impediments while trying to extract value from data. This was primarily due to the rapid growth of many carriers through mergers and acquisitions without paying proper attention to integrating front- and back-end systems while standardizing data. As a sign of better things to come, new fintech players such as Planck Resolution have “cracked the code” and have demonstrated real, scalable results in commercial insurance, integrating AI to legacy systems and delivering measurable value quickly. “We approached this issue, by avoiding long integrations on the customer side, and allowing them to use a simple API, while any required configuration is done on our end,” says Elad Tsur, CEO & Co-Founder at Planck Resolution.

For commercial insurers who want to reduce their loss ratios, increase conversion rates, grow sales, while reducing operational costs, the company has developed a disruptive platform that helps them achieve these results without changing their underwriting model and fields. The AI-based platform generates real-time, automated underwriting insights for commercial insurers with an accuracy and coverage of more than 90 percent. “The combination of rich, granular, up-to-date, data in the open web with AI and machine learning capabilities enables insurers to change the game,” adds Tsur. The platform enables clients to pre-fill and eliminate questions from questionnaires, to streamline the underwriting process while reducing loss ratio. Their clients can also leverage the platform to explore, collect, and analyze new data fields quickly and at a marginal cost, to generate smarter insight and better pricing. Clients can test the platform’s coverage and accuracy by running a quick POC, without any integration to see the value before writing a single line of code on the client’s end.

“To provide the carriers with up-to-date and highly accurate real-time data insights about any business, the platform runs a 3-phase process,” says Tsur. The platform first collects the data from thousands of resources. It then extracts the relevant information from the collected data through AI capabilities such as computer vision, NLP, semantic analysis of unstructured data and more.

The combination of rich, granular, up-to-date, data in the open web with AI and machine learning capabilities enables insurers to change the game

The last phase includes bringing together all those pieces of information to find the truth about the businesses. These underwriting insights are sent within seconds to the policy admin systems via an API.

One of the critical elements that differentiates Planck Resolution from their competitors is the ability to use technology to get the most up-to-date and relevant data insights. “The majority of data sources that are available today for carriers are based on what we call “Static” data bases”, says Tsur. “They collect the data whenever they collect it, and can provide only answers on whatever fields that are plainly defined there. We’ve built a “dynamic” data platform where insights are being created in real-time. This ensures that the business data is up-to-date and can be configured to address any underwriting question.”

Tsur denotes an instance where the company had been able to help a commercial insurer. The applications they received were only 50 percent complete and the accuracy of the fields provided was less than 60 percent. Planck Resolution was able to answer all underwriting questions with 92 percent accuracy for 95 percent of the submissions. “That carrier is now one of our many production customers, and achieves those brilliant statistics in real-time on all submissions,” adds Tsur.

“We are solely focused on commercial insurance, and we have built an extraordinary, amazing team of top engineers and data scientist talents,” says Tsur. “Down the road, we plan to expand the product and leverage its capabilities for insurance-related marketing purposes like using it for positive selection, fraud detection, claims adjustments and more.”