Quote: Data Access is Key for Modern Underwriters
The role of the underwriter is evolving to require immediate access to comprehensive data, according to Abel Travis, host of the Insurance Innovators Unscripted podcast. "The underwriter of the future expects all data at their fingertips—third-party evidence, AI risk scores, and real-time alerts on policy changes," Travis said. He noted that vendors who can deliver relevant data quickly will have a competitive advantage.
- The shift to data-driven underwriting is moving the industry away from a reliance on historical data and generalized actuarial tables. Instead, it is moving toward analyzing real-time, diverse datasets which can include information from Internet of Things (IoT) devices, social media, and electronic health records. - Artificial intelligence is significantly speeding up the underwriting process, with some insurers reporting a 70% faster application processing time. AI-powered systems can reduce the average decision time for standard policies from several days to just over 12 minutes while maintaining a high accuracy rate in risk assessment. - The role of the underwriter is evolving from a risk assessor to a risk advisor and data owner. This requires a deeper understanding of a client's business and the ability to use data to predict unforeseen events and identify protection gaps. - Abel Travis, the podcast host quoted, is also the Vice President of Underwriting and Product Innovation at AF Group and has over 15 years of experience in the insurance industry. He also advises technology startups and private equity firms that are investing in the insurance sector. - To facilitate this new underwriting approach, many carriers are investing in underwriting workbenches. These platforms provide a single dashboard with a 360-degree view of an underwriter's portfolio, integrating various data sources and analytics tools. - A significant challenge in implementing AI-driven underwriting is the potential for bias in the algorithms. If the data used to train the AI is biased, the resulting risk assessments will also be biased, which raises ethical considerations and the risk of data misuse. - Insurers are increasingly using a combination of structured and unstructured data to create more accurate risk profiles. Structured data includes traditional information like credit scores and claims history, while unstructured data can be text from emails, images, and social media content. - The adoption of new data sources and technologies is creating a demand for underwriters with more specialized skills. This includes a deep understanding of data governance and the ability to interpret complex data from various sources, such as clinical labs and medical claims.
