(Image credit: Jane Snyder.)
Peak enrollment period is here once again as group and voluntary benefits providers put their remote work arrangements to the test in what will be an unusually demanding season. This year has been the year of digital transformation in the insurance industry, and 2020’s challenges will inspire new approaches and digitization for enrollment. Fortunately, insurers can use AI and predictive analytics to streamline quoting and enrollment, optimize resources, and automate manual tasks.
Streamline Quoting with Predictive Analytics
Traditional renewal processes raise several speedbumps. Disconnects between quoting and underwriting as well as unreliable information on past successful plan designs unnecessarily increase quote turnaround time, resulting in missed opportunities and a poorer customer experience. Increased competition is driving carriers to pursue data-driven solutions to these problems.
This is where predictive analytics comes in. During quoting, insurers can leverage machine- learning algorithms to process historical or synthetic data to identify the most successful sold plan designs for particular group sizes and SIC codes, speeding up the sale of a new plan. Using artificial intelligence to generate a recommended alternative quote provides a valuable benchmark based on reliable data and reduces guesswork.
For renewals, AI can be trained to make suggestions for upselling and cross-selling opportunities on optional worksite products, while taking census demographics and experience and claims history into account. AI can be used on voluntary products as well, using health and demographic data to recommend the best supplementary benefit selections. These tools allow carriers to tap valuable existing sources of revenue in uncertain times.
A Voya Financial survey found that 53 percent of American workers plan to make changes to their employee benefits during open enrollment this year, and 7 in 10 plan on spending more time reviewing their benefits than they did in 2019. (In Canada, enrollment is spread throughout the year, but still, employees are looking more closely at their benefits.) Given these trends, carriers that implement predictive analytics to pursue upselling and cross-selling opportunities will experience a highly successful enrollment season.
Optimize Underwriting Resources
Maximizing the value of a carrier’s underwriting department during enrollment requires a careful blend of managerial oversight and underwriter performance data. Unfortunately, things get busy. As a result, this process is rarely realized to its fullest potential, resulting in suboptimal quote turn-around-time. During high-load periods, the high volume of quotes requiring underwriter review can slow down processes due to an inefficient allocation of human resources.
With AI, workload recommendations can now be generated automatically. Carriers can train machine-learning models to assist underwriting managers in suggesting the most effective distribution of quotes across the underwriting team.
AI can take an individual underwriter’s current capacity and performance history into account when making recommendations. Additionally – and this is really cool – it can be used to prioritize quotes with the highest chance of closing based on past successes. This is similar to the use of predictive analytics for new benefits plan design mentioned in the last section but on the underwriting side.
Automate Manual Tasks, Process RFPs Faster
The influx of Requests for Proposal (RFP) that come in can produce unwanted friction and increase quote turnaround time. For example, a group carrier can receive RFPs in many different formats including email, Word documents, Excel spreadsheets, or PDFs with text or embedded images. Traditionally, it has been a labor-intensive process to convert a high volume of RFPs arriving in different formats into a format that internal systems can comprehend.
With any labor-intensive process comes a powerful opportunity for AI. AI systems are capable of extracting information from RFP source files using various machine learning algorithms in conjunction with optical character recognition (OCR) and other techniques to identify and process information trapped in images and other formats.
Different file formats are not the only hindrance to faster quote turnaround time. Variations in how we use language and terminology to define a single concept require human knowledge to untangle.
Natural Language Processing
NLP, the branch of artificial intelligence that deals with how machines can process large amounts of human language data, will play a key role in automating this process. NLP can be used to train an AI system to “read” an RFP booklet and learn carrier-specific terms and abbreviations such as naming conventions for product divisions and classes.
AI systems can even learn to generate a quote based on the information detected within the source document. There are very few aspects of the enrollment process that AI cannot touch.
Winning the 2021 Enrollment Season
Annual enrollment for 2021 will be a major opportunity for carriers to sell new group and voluntary products due to shifting employer and employee attitudes throughout the pandemic. The demands of this year’s enrollment season should also motivate carriers to re-evaluate their ability to innovate and straight-through process quotes, and consider adopting artificial intelligence to remove bottlenecks within their workflow.
Employees will likely spend more time reviewing their benefits whenever they happen to be up for renewal. They’ll demand increased speed and personalization from their benefits providers (both of which can be facilitated by AI).
Group life & health insurers have only scratched the surface when it comes to adopting artificial intelligence in the enrollment process. Carriers that can successfully fill this innovation gap will find their organizations in a strong position to compete and win in the “new normal.”