As the commercial insurance industry continues to evolve, insurers are under increasing pressure to streamline their operations, reduce costs and improve user experiences for agents AND insureds. The discipline of underwriting, involving assessing and pricing risk, is a critical function in this process, and the need for efficiency and thoroughness has never been greater.
One way insurers are addressing these challenges is by leveraging novel data, created by artificial intelligence (AI) to automate underwriting processes. By using machine learning algorithms to analyze vast amounts of data to predict the answers to underwriting questions, insurers can reduce the time and effort required to underwrite policies, while improving accuracy and consistency. It’s not too dissimilar from the way large language models (LLMs) like ChatGPT and Google’s Bard use vast amounts of unstructured web content to predict the next word in a conversational sequence.
Automation and data-driven decision-making also allow commercial underwriters to focus on more complex risks, while leaving routine and straightforward applications to be handled by machines. This not only increases efficiency but also allows underwriters to spend more time on high-value tasks that require their expertise and judgment. This is a MASSIVE win for underwriters who are forced into tactical, rather than strategic roles, manually processing applications rather than making value-added risk evaluations. In a low-complexity line of business like workers' compensation, a client of Planck was able to decrease processing time from hours to minutes and reduce submission errors by 29%, ultimately leading to a hit rate increase of 19%!
Underwriting automation also improves risk management. Machine learning algorithms can identify patterns and trends that human underwriters may miss, allowing insurers to better predict and manage risk. This can lead to more accurate pricing, as well as fewer claims and losses for the insurer.
AI in underwriting also allows for more personalized risk assessments, which can lead to better pricing and coverage for clients. Insurers can use data from a variety of sources, including social media, satellite imagery and weather data, to gain a more complete picture of risk and tailor policies accordingly. This can help underwriters better understand the specific needs of each client, while also providing them with more targeted recommendations. Through a partnership with a top-three European carrier, Planck was able to identify that 75% of this carrier’s construction book of business was underinsured — leading to a 30% potential increase in revenue.
See also: Insurers Boosting Their Use of AI
Underwriter augmentation of this sort is not a silver bullet, and it is not intended to replace underwriters entirely. Instead, if correctly deployed, AI serves to magnify underwriter abilities, enhance their effectiveness and ultimately make their jobs easier.
Underwriting automation driven by AI-generated data can help make commercial underwriters more efficient, accurate and focused. By leveraging technology to streamline processes, insurers can improve customer service, reduce costs and better manage risk.