In 2020, we find ourselves living in a world that demands a real-time shopping experience. Brands like Amazon make this experience as easy as possible by providing the option to compare one product against another product(s). The comparisons include price, features and the length of time it will take for the product to arrive. Furthermore, we can see recommended products based on buying behavior patterns, as well as related products that can be purchased to maximize the overall value. Each of these factors weigh into how, when and from whom we purchase.
Behind the scenes of Amazon’s user experience are two key technologies driving innovation: artificial intelligence (AI) and machine learning (ML). These terms are not often tossed around when referring to the current group insurance shopping experience, although there is certainly much room for carriers to integrate these innovations to their benefit. The McKinsey Global Institute reports that up to 60% of insurance sales and distribution tasks could be automated, as well as up to 35% of underwriting tasks.
Herein lie three major machine learning opportunities to unlock a better user experience for all stakeholders in the purchasing process, from sales representatives and underwriters to brokers, employers and employees.
1. Automating Broker Emails and Required Quoting Documents
Imagine if Amazon required you to email a request every time you wanted to purchase a product, without knowing when the product would arrive, how much it would cost or whether it would even be shipped at all until three to five days after sending the original email.
In many cases, this is the experience today for brokers who email a request for proposal (RFP) to a group insurance carrier. And so we arrive at our first opportunity for machine learning; speeding up the quote turnaround time (TAT) by automating the setup of broker emails and documents required to quote. As we peel back the onion to see how most life and disability and worksite group carriers receive and process quote requests today, it is clear how manual the current process is. This process often entails inputting data twice; once in a CRM such as Salesforce, and a second time in a quoting and underwriting engine, or spreadsheet on macro steroids.
Much of this process can be automated by leveraging machine learning to train a model that runs through thousands of previous broker email RFPs to understand broker requests, the differences between brokers and what information is required to quote the desired products. Oftentimes, brokers do not provide all the information necessary for quoting, which today is handled by placing the group “on hold.” The RFP intake specialist then has to manually email the broker back and ask for the missing information to proceed with the quote request. Machine learning can help to quickly identify what is missing, and automatically reply to the broker requesting this information and drive to completion.
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2. Automating Plan Design(s) to Quote
Many times the RFP includes a current coverage contract or booklet that could be anywhere from 30 to 50 pages. This document contains all the clues as to which plan design should be quoted to compete with the carrier currently in force. The foundational plan design to quote starts with matching up the exact benefits for each product line and, you guessed it, going line-by-line through that 50-page contract booklet to manually hand-stitch a plan design to quote. As you can imagine, this is not the most efficient experience for the RFP intake specialist, nor the broker who ends up receiving a quote riddled with manual errors and plans that do not match up with the customer's current coverage.
In this case, a machine learning model can be trained to extract all the plan design elements from any incoming file that contains current coverage details. This ML model would be able to decipher the current carrier’s format structures and benefit naming conventions, and subsequently translate them into the quoting carrier’s structure. Of course, there are instances in which a customer's current plan design is not able to be quoted, sold and administered. In this case, a machine learning model would be able to flag any benefits that aren’t able to be translated and accounted for. To get the maximum value, this use case assumes an API integration with a quoting engine to automate plans to quote.
3. Analyzing Closed-Won and Closed-Lost Proposals
At the moment, once a case has been either sold or lost, most carriers are not harnessing the true power of the resulting data (i.e. the insights and components required to make a winning proposal.) Carriers tend to look more closely at closed-won proposals because they have to use this data to implement policies and sold rates. But even here, the data currently being captured and tracked leaves much room for improvement.
Machine learning and AI models can be used here to better analyze which RFPs are the most likely to win based on a variety of factors. For example, an ML model could track the current carriers and rates on incoming RFPs and gather won/lost data once the sale has closed. This data can be used to inform which future RFPs are most likely to win based on the customer's current carrier.
On the flip side, closed-lost proposal data (that now typically ends up in an abyss far from any BI visualization tools) could be used to show key factors as to why the case was lost. A national life and disability carrier focused on the small group sector may have around 100,000 RFPs a year. If the close ratio is 9%, that means 91,000 proposals were lost. These thousands of proposals could be fed into a machine learning model to analyze their ingredients, in the hopes of adjusting the sales recipe to increase future close ratios.
A More Profitable Future
Opportunities for ML and AI implementation within the group industry are evident, and these use cases will ultimately enhance the user experience as well as service policies, manage billing, process claims and handle renewals. 46% of AI vendors in insurance offer solutions for claims, and 43% have solutions for underwriting; the solutions have been far more widely used within the home and auto industry than in the group insurance sector. One important part of this approach is to identify where the "lowest hanging fruit" use cases exist, which can be implemented in a proof-of-concept fashion.
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The implementations can either be achieved with internal teams or by working with insurtech partner solutions. The first and second ML opportunities outlined both exist within the RFP intake process, which can provide direct operating savings ROI, whereas the third may take longer to actualize as close ratios gradually increase. To move toward a more profitable future, it is essential that group carriers notice and take full advantage of the advancements being made in machine learning technology today.
