How to Reduce Commercial Lines Underwriting Leakage

Artificial Intelligence - November 10 2020

Underwriting leakage can erode competitive advantage, impact distribution channels, and impede growth. In a hardening market, insurers need to be able to capitalize on every opportunity that meets their risk appetite, be more competitive on preferred risks and maintain healthy loss ratios. Missed opportunities negatively impact a book of business, brand reputation, and customer relationships.

Evaluating how likely it is that the insured will make a costly claim and whether the insurer will lose money by issuing the policy, requires underwriters to use rigorous processes supported by vast amounts of data to ascertain the risk. Without access to the right data and the means to identify the right business that meets the risk appetite quickly and efficiently often results in underwriting leakage.

Today, underwriting teams spend copious amounts of time hunting, pecking, and searching through hundreds, even thousands of submissions daily manually extracting key data points needed to accurately assess the risk, and determine the right pricing. Sifting through emails and long attachments is extremely time-consuming and frustrating for knowledge workers, who’s time could be better spent on value-add initiatives, rather than focused on manual processing emails which often results in duplication of effort and a agonizing experience for the insurer, insured and brokers due to the lack of speed, transparency, and ease of doing business. 

On average, processing a submission can take up to ten business days or two weeks of elapsed time, from the time the submission or application arrives in an email inbox or portal. Submissions typically sit in a queue for 3 to 5 days while a junior underwriter or underwriting technical analyst applies the underwriting principles set out by the Chief Underwriting Officer. Typically spending 2 to 4 hours entering a subset of data manually pulled from the submission into various underwriting systems such as quoting, clearance and registration, CRM, policy administration, etc., before they send the submission to the appropriate underwriter to actually assess the risk. Sound familiar? Like any manual process, it's not perfect, often resulting in missed opportunities.

Insurers seeking to reduce underwriting leakage to zero should consider automating their clearance and registration process as well as improving the quality and comprehensiveness of their data. By leveraging AI solutions purpose-built for insurance, insurers can apply intelligent automation to their submission intake process enabling underwriters to identify the right business that meets their risk appetite quickly and efficiently. AI solutions that perform real-time data extraction of hundreds of data points help insurers differentiate themselves among their competitors by empowering underwriters with access to data previously unavailable to them revealing more of the risk characteristics and the insights needed to write more profitable business.

Fine-Tuning the Underwriting Process with AI

Automating the submission intake process and applying intelligence to triage submissions allows insurers to trigger rapid responses, prevent underwriting leakage, and free up their underwriting teams to focus on high-value strategic initiatives. AI solutions enable underwriters to make better data-driven risk decisions, avoid potential misclassifications, optimize pricing, and capitalize on opportunities.

With AI the submission intake process can be shortened significantly from weeks to days, enabling commercial insurance carriers to identify the best business to underwrite quickly and efficiently, eliminating missed opportunities. Imagine if an underwriter had instant access to more than 15 data points, they would be able to take make better decisions faster on risk assessment and pricing.

AI solutions powered by Natural Language Processing (NLP) and Machine Learning (ML) can automatically read and process unstructured emails and attachments. The technology can read, extract and interpret unstructured data from digital insurance documents such as emails, Word, Excel and PDF faster than a human eliminating the need for underwriters to spend countless hours reviewing and manually extracting data from submissions and re-keying data into rating engines, CRM, policy administration, etc. Automating time-consuming processes in the underwriting workflow that do not require judgment frees up underwriting staff to spend more time evaluating and assessing the right opportunities.

Giving underwriters access to better information, gives them a holistic view of the risk so they can better assess the risk faster and with more certainty. If insurance companies have a better archive of information and better tools to access information, their underwriting leakage will be smaller, they can price more aggressively, and they will be able to understand risks better. Preventing underwriting leakage with AI, gives insurers that ability to build portfolios of risk that lead to better business results.

The Link Between Data Quality and Underwriting Leakage

Insurance is one of the most data rich industries in the world. Insurers collect petabytes of data annually with 80% of this data unstructured residing in data silos across the organization and only accessible manually. With limited access to data, underwriters make risk selection decisions on a mere 15 to 50 data points. Even though insurers collect hundreds of data points on every submission, existing processes prohibit underwriting teams from accessing more data, impeding their ability to quickly select or decline business.

AI can help insurers eliminate data silos and give underwriting teams greater access to hundreds of data points in real-time to accurately assess how risky it is to issue coverage for a company or business based on unique circumstances such as the type of business, revenue, location, claims history, etc. By automatically reading submissions the same way humans do by sections, tables, and paragraphs, to extract key data such as policyholder name, city, state, country, producer, limits, premiums, coverages, effective dates, dollar values, etc., and feed this data into underwriting systems, underwriting teams have access to greater data to make informed decisions to decline or write the business. Investments in better data, better models and better tools can help reduce underwriting leakage.

Technology Adoption

To plug the leaks in the commercial underwriting process, progressive commercial lines insurers are adopting AI to intelligently automate their submission intake process. AI enables insurers to automate the process of extracting data from submissions at the time of the submission, auto-populate clearance and registration systems, and auto-decline or auto-route the submission to the appropriate underwriter within minutes. With greater access to hundreds of data points, underwriters can make better risk selections, write more business that meets the risk appetite faster, increase response times, and deliver a better customer experience.

AI enhances the underwriting process by providing consistency and efficiency. Through automation, insurers can reduce the time it takes to triage submissions from several weeks to minutes. Being able to auto-decline submissions by sending an email notification to distribution partners quickly and efficiently maintains relationships and delivers a better brand experience. Identifying the right business quickly and auto routing the submission to the appropriate underwriting team enables insurers to increase boost their quoting capacity by 50%.

See for Yourself

To learn how AI can help you automate and streamline the submission intake process, watch our two-minute video below.

Chisel AI - Submission Triage

Watch the video explaining how automated submission intake works

 

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