How insurers can boost underwriting with quality data
Prioritizing data-driven underwriting modernization extends beyond large insurance organizations, especially for commercial business. Midsize and growing insurers are increasingly adopting artificial intelligence and large language models to enhance access to critical risk-quality data for underwriting proficiency. Insurers of all sizes, as well as managing general agents and program administrators, are taking advantage of intelligent data-delivery platforms.
It’s not just insurers that must modernize precise risk assessment and quoting workflows. Insurance distribution channels also play a vital role for intermediate-level insurers, particularly in small and medium commercial markets. Business owners frequently require guidance and personalized attention from agents and program administrators/MGAs to ensure they obtain the right coverages. Clearly, policyholder expectations are evolving rapidly, with demands for quicker, accurate quotes as well as efficient policy issuance, service, and renewal management.
With cognitive technologies and the vast scope of reliable data they can source and supply, insurers and their distributors now have the means to deliver a seamless experience for policyholders. By harnessing the power of generative AI for real-time exposure data, insurance organizations can offer precision policy pricing and exceptional service to agents and MGAs, including application prefill during the quoting process for standard lines of business. Simultaneously, this technology helps expand portfolios by identifying profitable accounts that align with the risk appetites of insurers and their distributors.
Distribution partners also require a dynamic understanding of their small and medium-sized clients, whose operations, products, and services are constantly evolving. Agents and MGAs often seek to collaborate with insurers who can perform comprehensive assessments of exposures and ensure policyholders have appropriate coverages. With accurate classification and risk-assessment data supplied by generative AI platforms, underwriters can complete business assessments within minutes.
Additionally, for smaller and midsize insurers, generative AI data sourcing enables business expansion without the necessity to increase underwriting resources. Insurers with limited underwriting resources can quickly use generative AI platforms for essential data insights to process more quotes accurately. They can rapidly uncover in-appetite leads and evaluate renewal accounts for premium leakage. Underwriting assistants can better handle accounts with standardized risks, and senior underwriters can deploy AI-powered data insights to assess risk tolerance for more complex accounts.
Incorporating data from generative AI in underwriting workflows
There is no one-size-fits-all approach when incorporating the data assets of generative AI technology into underwriting workflows. However, a few best practices can assist in selecting and integrating intelligent data platforms to boost underwriting capabilities.
1. Evaluate existing systems and processes: Before incorporating the data assets of generative AI into underwriting workflows, ensure the flexibility of core systems to seamlessly integrate with third-party solutions and data via APIs. Instead of adopting a piecemeal approach, carefully consider how third-party data can be incorporated effectively.
Realistically assess current processes and determine how technology and enhanced data integration can fit into existing or expected workflows. If quoting processes are already robust, incorporating risk-assessment data swiftly will simply expedite the workflow. However, less structured quoting processes may pose integration challenges or require additional operational considerations to be addressed first. Candidly addressing current capabilities will help preparation for upcoming changes to maximize value from investments in higher levels of risk-quality data acquisition.
2. Align AI data outcomes with distributor network needs: Engage and understand the primary requirements and challenges of agents or program administrators /MGAs in serving policyholders. By identifying their needs — such as faster and more accurate risk classification or an expedited renewal process — the integration of intelligent data platforms can be shaped and prioritized accordingly.
3. View the data output of generative AI as a growth engine: While focusing on underwriting processes as one critical benefit of real-time risk-quality information, the implementation of generative AI-powered data can also diversify the path for business expansion. Weigh the value of dynamic, data-driven analysis on book-of-business flexibility and resilience. By planning well, the incorporation of intelligent data products can be a natural boost for enterprise growth.
4. Ascertain personalization capabilities in data platforms: In technology and data implementations, insurers of all sizes need diverse levels of personalization. Modern solution providers should be able to tailor their offerings to align with specific risk-qualification requirements. For instance, to avoid writing restaurant businesses which have dance floors or in-house delivery services, the solution provider should accommodate these specifications with relevant risk-quality questions to uncover reliable and transparent data to validate exposure limits.
At the same time, concerns about personalization delaying the integration of AI-powered data can be mitigated. Solution providers should be able to offer a turnkey solution, but with the flexibility to make bespoke modifications easily and cost-effectively, based on specific or evolving business priorities.
5. Emphasize visibility and transparency of data sources: To address concerns among distribution partners and underwriters about data integrity, prioritize transparency for the sources of classification and risk-quality data from generative AI technology. Partner with solution providers that feature built-in tools to clearly validate the source of answers to risk questions. Complete data transparency addresses regulatory compliance, builds trust between underwriters, agents or program administrators, and policyholders, potentially acting as a decisive factor in establishing insurers of choice for distribution networks.
Distributors and policyholders increasingly expect seamless and efficient experiences and accurate, timely quotes from insurers. By integrating generative AI technology and thoughtfully implementing the resultant data about risk quality, insurance providers can gain deeper understanding of commercial exposures, align risk tolerance to optimize product portfolios, while meeting and exceeding policyholder expectations.
