Authors: Rachit Dhir
Small-and-medium-sized businesses often embark on unrewarding insurance journeys. There are about 28 million such businesses in the US that require at least 4-5 types of insurance. Over 70% of them are either underinsured or have no insurance at all. One reason is that their road to insurance coverage can be long, complex, and unpredictable. While filling out commercial insurance applications, SMB owners face several complicated questions for which crucial information is either not readily available or poorly understood. Underwriters, however, need this information promptly to estimate risks associated with extending the coverage. It makes the overall underwriting process extremely iterative, time-consuming, and labor-intensive.
For instance, business owners need to answer over 40 different questions when they apply for worker’s compensation insurance. In addition, it could take many weeks of constant emailing between insurance companies and businesses after submission! Such bottlenecks lead to poor customer experiences while significantly impacting the quote to bind ratio for insurers. Furthermore, over 20% of the information captured from businesses and agents is inaccurate – resulting in premium leakage and poor claims experience.
The emergence of data prefill – and the challenges ahead
Today, more insurers are eager to pre-populate their underwriting applications by using public and proprietary data sources. The data captured from external sources help them precisely assess risks across insurance coverages, including Workers Compensation, General Liability, Business Property, and Commercial Auto. For example, insurers can explore company websites and external data sources like Google Maps, OpenCorporates, Yelp, Zomato, Trip Advisor, Instagram, Foursquare, Kompass, etc. These sources provide accurate details, such as year of establishment, industry class, hours of operation, workforce, physical equipment, construction quality, safety standards, and more.
However, despite the availability of several products that claim to have successfully prefilled underwriting data, insurance providers continue to grapple with challenges like evolving business needs and risks, constant changes in public data format, ground truth validation, and legal intricacies. Sources keep evolving over time both in terms of structure and data availability. Some even come with specific legal constraints. For instance, scraping is prohibited by many external websites. Moreover, the data prefill platform needs to fetch data from multiple sources, which requires proper source prioritization and validation.
Insurers have thus started to consider building custom white-box solutions that are configurable, scalable, efficient, and compliant.
Creating accurate, effortless, and fast underwriting journeys
The futuristic data prefill platforms can empower business insurance providers to prefill underwriting information effortlessly and accurately. These custom-made platforms are powered by state-of-art data matching and extraction frameworks, a suite of advanced data science techniques, triangulation algorithms, and scalable architecture blueprints. The platform empowers underwriters to directly extract data from external sources with a high fill rate and great speed. Where the data is not directly available, the ML classifiers help predict underwriting questions for underwriters with high accuracy.
Tiger Analytics has helped insurers to custom-build such AI-led underwriting data prefill solutions to support various underwriting decisions. Our data prefill solution ensures increased speed-to-market and scalability – with improvements gained through incremental addition of each source. It is a highly customizable white-box solution with no IP rights – and a codebase that can be quickly and cost-effectively tweaked to cater to any changes in external source formats. Delivered as a cloud-hosted solution, this solution uses Lambda architecture to enable scale and state-of-the-art application orchestration engine to prefill data for underwriting purposes.
• Unparalleled accuracy of 95% on all the data provided by the platform
• Over 90% fill rate
• Significant cost savings of up to $10 million annually
• Accelerated value creation by enabling insurers to start realizing value within 3-6 months
Insurers must focus on leveraging external data sources and state-of-the-art AI frameworks, data science models, and data engineering components to prefill applications. And with the right data prefill platform, insurers can improve the overall quote to bind ratio, assess risks accurately and stay ahead of the competition.Tags: Insurance Analytics Insurance Analytics Trends