5 key cases for generating AI in insurance distribution | Insurance Blog
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 conversation. As an industry, we are in constant discussions about disruption, changing market factors – often beyond our control (eg, consumer expectations, capital market impacts, ongoing M&A) – and how best to address them. This includes the use of the latest equipment/tool/capabilities that promise more growth, better margins, more efficiency, increased employee satisfaction, etc. However, few of these solutions have found success in creating a major revenue-generating role change in the industry…until now.
Technology has been greatly improved to drive efficiency, and if adopted properly, there have been pockets of success; however, the people who need to use the technology or input the data that powers the information to drive efficiency are often the ones who gain little or nothing from the solution. At its core, GenAI has increased the accessibility of data, and has the potential to be the first technology to be widely adopted by revenue generating roles as it can provide actionable insight into organic growth opportunities with customers and carriers. Undoubtedly, it is the first of its kind to offer a tangible “what’s in it for me?” in revenue-generating roles within the insurance value chain that don’t give them more data, but the information they have to.
There are five key use cases that we believe demonstrate GenAI’s promise for consumers and agents:
- Useful “clients like you” analysis: In marketing businesses that have grown significantly through acquisitions, it is often difficult to identify similar client portfolios that can provide up-selling and up-selling opportunities to the acquired agencies. With GenAI, comparisons can be made to the agency’s business books acquired across locations, acquisitions, etc.
- Submission preparation and client portfolio QA: For buyers and/or agents without national practice groups or specialty industry groups, insurers in industries outside of their primary strike area often present challenges in terms of asking the right questions to understand exposure and coverage. The effort required to identify adequate installations and prepare for deployment can be greatly reduced with GenAI. Specifically, this technology can help inform the broker/agent of the types of questions to ask based on what is known about the insurer, the industry the insurer operates in, the risk profile of the insured relative to others, and what is available in 3rd group data sources. In addition, GenAI can act as a “spot check” to identify potential overlooked or over-market opportunities and support E&O reduction. Historically, the quality of portfolio submissions and subsequent submissions can be at the discretion of the producer and the account team managing the account. With GenAI, years of knowledge and experience on the right questions to ask can be at the seller and/or agent’s hands, serving as a QA and sales and marketing tool.
- Placement wise: Risk placement decisions for each client are largely driven by account managers and producers based on the level of relationship with the carrier / underwriter and the carrier’s known or perceived interest in the client’s risk portfolio. While the wealth of knowledge gained over years of experience in placement is significant, the changing risk appetites of carriers due to ongoing changes in client risk profiles make finding the right placement for agencies and vendors challenging. With the help of GenAI, agencies and vendors can compare a carrier’s stated desire, customer risk and policy recommendations, and agency or vendor financial contract details to create a delivery summary. This provides the account team with placement recommendations that are most favorable to the client and the agency or vendor while reducing time spent on marketing, both in terms of finding the right markets and avoiding markets where risk cannot be accepted.
- To avoid loss of income: As clients prefer advisory fees instead of commission, fees that are not direct, but attributed to specific risk management activities to be provided by the agency or broker are often charged “under”. GenAI as a capability could theoretically import client contracts, review fee-based services agreements internally, and create a summary that could be provided through a tool such as an internal information exchange to account staff. This information management solution can provide specific guidance to the employee, at the time of need, on what payments should be charged based on contractual obligations, providing an opportunity for increased revenue for agencies and consumers with unknown, uncollectible receivables.
- Customer-specific marketing materials for speed: Historically, if an agent or salesperson wanted to expand non-core capabilities (eg, digital marketing) they would hire or hire talent to get the right expertise and the right return on the effort. While this worked, it led to an increase in SG&A that could not be tightly tied to growth. GenAI-type solutions offer a solution to this because they allow the agent or salesperson scalable access to non-core skills (such as digital marketing) at a fraction of the investment and cost and with a potentially better result. As an example, GenAI results can be customized at a rapid pace to enable agencies and marketers to generate industry-specific content for mid-market clients (eg, we cover X% of the market and Z number of your peers) without the timely effort of creating a single sales collateral and execution.
Although the use cases we’ve described are still in the prototyping stage, they paint a picture of what the near future could look like as man and machine combine to achieve revenue-generating activities. There are three key steps we encourage all our broker/agent clients to take next as they explore the use of this technology in their workflow:
- Focus on a small data set: Leveraging GenAI requires some data to be more reliable in order to generate actionable insights. A common misconception is that it has to be all agent or vendor data to use GenAI, but the truth is start small, implement, and expand. Identify the data elements most important to the desired insights and develop data management and cleansing strategies to optimize that dataset before expansion. Doing so will give the secret computing models a set of data to work with, providing business value, before expanding data sanitization efforts.
- Prioritize driver use cases: As with most emerging technologies, the value delivered through use cases is being tested. Buyers and agents should assess what high-value use cases are possible and create pilots to test value in those areas with a feedback loop between the development team and revenue generation teams to make adjustments and changes as needed.
- Measure the handling and reception: As we have discussed, insurance as an industry is slow to adopt new technologies and, as a result, sellers and agents must be ready to invest in management change and the necessary adoption strategies to show how this technology can be the first of its kind to impact revenue and organic growth in a positive way for revenue generating groups.
While this blog post is intended to be a partial overview of how GenAI could impact distribution, we have many thoughts and opinions on the matter, including implications for underwriting and claims for both carriers and MGAs. Please get in touch Heather Sullivan or Bob Besio if you would like to discuss further.
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