5 key generative AI use cases in insurance distribution
GenAI has taken the world by storm. You can’t attend an industry conference, participate in an industry meeting, or plan for the future without GenAI entering the discussion. As an industry, we are in near constant discussion about disruption, evolving market factors – often outside of our control (e.g., consumer expectations, impacts of the capital market, continued M&A) – and the most optimal way to solve for them. This includes use of the latest asset / tool / capability that has the promise for more growth, better margins, increased efficiency, increased employee satisfaction, etc. However, few of these solutions have achieved success creating mass change for the revenue generating roles in the industry…until now.
Technology has largely been developed to drive efficiencies, and if properly adopted, there have been pockets of achievement; however, the individuals required to use the technology or enter in the data that powers the insights to drive the efficiencies are often the ones who reap little to no benefit from the solution. At its core, GenAI has increased the accessibility of insights, and has the potential to be the first technology widely adopted by revenue generating roles as it can provide actionable insights into organic growth opportunities with clients and carriers. It is, arguably, the first of its kind to provide a tangible “what is in it for me?” to the revenue generating roles within the insurance value chain giving them not more data, but insights to act.
There are five key use cases that we believe illustrate the promise of GenAI for brokers and agents:
Actionable “clients like you” analysis: In brokerage businesses that have grown largely through amalgamation of acquisition, it is often difficult to identify like-for-like client portfolios that can provide cross-sell and up-sell opportunities to acquired agencies. With GenAI, comparisons can be done of acquired agencies’ books of business across geographies, acquisitions, etc. to identify clients that have similar profiles but different insurance solutions, opening up material insight for producers to revisit the insurance programs for their clients and opening up greater organic growth opportunities powered by insights on where to act.
Submission preparation and client portfolio QA: For brokers and/or agents that don’t have national practice groups or specialized industry teams, insureds within industries outside of their core strike zone often present challenges in terms of asking the right questions to understand the exposure and match coverage. The effort required to identify adequate coverage and prepare submissions can be dramatically reduced through GenAI. Specifically, this technology can help prompt the broker/ agent on the types of questions they should be asking based on what is known about the insured, the industry the insured operates in, the risk profile of the insured’s company compared to others, and what’s available in 3rd party data sources. Furthermore, GenAI can act as a “spot check” to identify potentially overlooked up-sell or cross-sell opportunities as well as support mitigation of E&O. Historically, the quality of the portfolio coverage and subsequent submission would be at the sheer discretion of the producer and account team handling the account. With GenAI, years of knowledge and experience in the right questions to ask can be at a broker and/or agent’s fingertips, acting as a QA and cross-sell and up-sell tool.
Intelligent placements: The risk placement decisions for each client are largely driven by account managers and producers based on level of relationship with a carrier / underwriter and known or perceived carrier appetite for the given risk portfolio of a client. While the wealth of knowledge gained over years of experience in placement is notable, the changing risk appetites of carriers due to near constant changes in the risk profiles of clients makes finding the optimal placement for agencies and brokers challenging. With the support of GenAI, agencies and brokers can compare a carrier’s stated appetite, the client’s risks and policy recommendations, and the financial contractual details for the agency or broker to generate a submission summary. This provides the account team with placement recommendations that are in the best interest of the client and the agency or broker while reducing the time spent on marketing, both in terms of finding optimal markets and avoiding markets where a risk would not be accepted.
Revenue loss avoidance: As clients opt for advisory fees over commission, the fees that are not retainer-specific, but attributed to specific risk management actions to be provided by the agency or the broker often go “under” billed. GenAI as a capability could in theory ingest client contracts, evaluate the fee- based services agreements within, and establish a summary that can then be served up on an internal knowledge exchange-like tool for employees servicing the account. This knowledge management solution could serve specific guidance to the employee, at the time of need, on what fees should be billed based on the contractual obligations, providing a revenue growth opportunity for agencies and brokers that have unknown, uncollected receivables.
Client-specific marketing materials at speed: Historically, if an agent or broker wanted to expand a non-core capability (e.g., digital marketing) they would either hire or rent the capability to get the right expertise and the right return on effort. While this worked, it resulted in an expansion of SG&A that could not be tied tightly to growth. GenAI type solutions offer a solve for this in that they allow an agent or broker scalable access to non-core capabilities (such as digital marketing) for a fraction of the investment and cost and a potentially better outcome. As an example, GenAI outputs can be customized at a rapid pace to enable agencies and brokers to generate industry-specific material for middle market clients (e.g., we cover X% of the market and Z number of your peers) without the timely effort of creating one-and-done sales collateral.
While the use cases we’ve drawn out are in the prototyping phase, they do paint what the near-future could look like as human and machine meet for the benefit of revenue-generating activities. There are three key actions we encourage all of our broker/ agent clients to do next as they evaluate the use of this technology in their own workflows:
Focus on a subset of the data: Leveraging GenAI requires some of the data to be highly reliable in order to generate usable insights. A common misconception is that it must be all of an agent or broker’s data in order to take advantage of GenAI, but the reality is start small, execute, then expand. Identify the data elements most critical for the insight you want and establish data governance and clean-up strategies to improve that dataset before expanding. Doing so will give the private computing models a dataset to work with, providing value for the enterprise, before expanding the data hygiene efforts.
Prioritize use cases for pilot: Like many emerging technologies, the value delivered through executing use cases is being tested. Brokers and agents should evaluate what the potential high value use cases are and then create pilots to test the value in those areas with a feedback loop between the development team and the revenue- generating teams for necessary tweaks and changes.
Evaluate how to govern and adopt: As we discussed, insurance as an industry has been slower to adopt new technology and, as such, brokers and agents should be prepared to invest in the change management and adoption strategies necessary to show how this technology may very well be the first of its kind to materially impact revenue and organic growth in a positive fashion for revenue generating teams.
While this blog post is meant to be a non-exhaustive view into how GenAI could impact distribution, we have many more thoughts and ideas on the matter, including impacts in underwriting & claims for both carriers & MGAs. Please reach out to Heather Sullivan or Bob Besio if you’d like to discuss further.
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