Insurance companies around the globe are facing one of their biggest challenges to date: a workforce shortage, magnified by the Baby Boomer generation’s retirement, the migration of skilled labor during the Great Resignation and shrinking pools of aspiring insurance professionals among Millennials and Generation Z.
According to the U.S. Bureau of Labor Statistics and the National Association of Mutual Insurance Companies, 50% of the current insurance workforce will retire in the next 15 years. In many cases, the highly nuanced knowledge these professionals have developed over the years will retire with them. This “retirement brain drain” is exacerbated by supervisors finding themselves increasingly short on time because they need to do more with fewer resources. Due to the considerable amount of time it takes to manage key tasks and processes, they have little time to train new employees, making this challenge more acute.
So, what can you do to safeguard your organization’s most valuable data, including the information that only lives in the heads of your most seasoned professionals? Hands-on training and job shadowing are incredibly important and have been used for decades to train new employees. However, they can only do so much to ensure relevant industry knowledge is preserved and passed on to the next generation of workers, especially given the high retirement rate of many veteran insurers.
To combat this growing problem, many insurers are turning to AI technology to supplement their current employee education and institutional knowledge management strategies. Specifically, they are training AI models with terabytes of data from industry data lakes along with their own internal datasets. This enables AI models to learn from a wide variety of scenarios insurers might encounter when underwriting or resolving claims. Many AI solutions now even ingest and learn from unstructured data—like the notes adjusters have made over the years about specific claims.
AI can serve as a virtual digital assistant, automatically scanning all aspects of claims data and updates to identify attributes or events that may affect claims’ cost and duration. Unlike a human, the digital assistant can work 24x7, read and understand millions of policies and claims, has total recall and never gets tired. It can detect a myriad of factors such as updated notes, comorbidities, psychosocial factors, recent medical tests and diagnoses that may affect a claim’s severity. Armed with this insight, the digital assistant can triage and prioritize claims, reducing claims cycle times and losses. It directs the adjuster to what needs the most attention and when to act to ensure maximum claimant satisfaction and profitability. Similarly, on the underwriting side, a virtual digital assistant can examine a policy and use its learnings from millions of other policies and third-party data to give underwriters deeper insight into policy risks to help them price more accurately.
The virtual digital assistant can give both underwriters and adjusters more confidence in making decisions. It provides valuable guardrails for the new underwriter or adjuster, and, for the experienced professional, AI amplifies what they already know and do, enabling them to accomplish more at a higher quality level with less work and time.
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AI Upskills the Insurance Industry
It’s encouraging and frankly exciting to see the 500-year-old insurance industry starting to lean into AI and other supportive technologies the same way they embraced digital transformation. We are now seeing AI grow in its adoption exponentially in both underwriting and claims management.
In a recent webinar, leaders from both MEMIC and Builders Mutual Insurance shared how their companies have benefited from using AI technology to upskill and knowledge-share with newer employees. Greg Jamison, senior vice president of underwriting at MEMIC, explained how AI models have helped the underwriting side of the business build dashboards to better understand and quantify underwriting risks. He remarked, “We leverage not only our own internal data but Gradient AI’s [industry data lake].” Ken Bunn, vice president of claims at Builders Mutual Insurance, found that AI enables new adjusters to benefit from models that have learned from millions of past claims and “provides guardrails for our newer folks. For example, new adjusters can learn from the predictions that the models make to help them better assess a claim’s severity and gain insight into how to properly reserve a specific type of claim."
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Using AI Models to Upskill Insurance Professionals
AI technology offers an increase of depth of training and knowledge-sharing beyond because it leverages expanded access to datasets. As more companies make AI part of their business operations, here’s what I expect they’ll find:
- AI leverages institutional data to mitigate lack of experience. AI captures institutional knowledge, learning from past claim decisions and resolutions, allowing new insurers to perform at a level commensurate with their more experienced co-workers.
- Less data gets lost in the shuffle. Traditional job shadowing can be time-consuming, prone to misunderstanding and data loss. When trainees leverage AI unified databases, they have access to a single source of truth, resulting in more accurate, consistent and profitable decisions.
- Industry data lakes can help insurers grow in new markets. When AI insurance models are trained using industry data lakes containing multiple insurers, geographies and industry segments, insurers can expand into new industries and geographies with confidence. By leveraging models trained on actual loss histories in new markets, insurers can quote new policies and manage claims more accurately.
- AI training tools create a better working environment for younger generations. The insurance industry needs to make the industry more interesting and attractive to younger generations. AI technology has the potential to reform the insurance industry’s staid reputation and position it as a technology leader, and help attract and retain younger staff.