Artificial Intelligence (AI): Mitigating Risk Related to the Insurance Industry Knowledge Transfer Challenge

Mitigating Knowledge Transfer Risk

Artificial Intelligence (AI): Mitigating Risk Related to the Insurance Industry Knowledge Transfer Challenge

It is well documented that a significant portion of the insurance industry workforce is rapidly approaching retirement. Because they have acquired experience and sharpened judgment through years of on-the-job training, this soon to be retired talent is often assigned to more complex or sever claims.  Surveys have also confirmed that Millennials have little understanding of or interest in the jobs that are available in the insurance industry. Thus, the industry faces a dual risk of not only finding sufficient interested candidates to fill jobs that will become available on retirement but also with replacing the knowledge and experience that departs when long term employees retire. 

To address this risk, some insurers focus on hiring primarily experienced professionals resulting in increased labor costs. This may be a short-term solution for some insurers in the industry that can absorb the costs or pass them on through rate increases but eventually retirements will out-pace the ability to replace with experienced talent. Moreover, the turnover as talent moves throughout the industry creates its own knowledge transfer challenge in the pending inventory that needs to be re-assigned.

Knowledge Transfer Risk Related to Turnover in the Pending Inventory

Artificial Intelligence (AI) coupled with Natural Language Processing (NLP) can mitigate the knowledge transfer risk. When talent turnover occurs, a pending claim portfolio must be re-assigned. These bulk re-assignments create significant challenges as it requires a claim professional to start over and review everything that has been collected in pending files. Most claim professionals find it more difficult to absorb and retain the important details of inherited claims compared to claims they have lived with since the first report. 

The primary objective of reviewing claim file data is to accurately identify the risk signals that can impact outcomes. AI and NLP can find those signals helping to expedite and focus the newly assigned professional on the data that matters. A well-designed end user interface driven by AI and NLP can expedite the process by reviewing the transferred file and validating the exposure and existing action plan. Moreover, it is far more efficient to review a pending file if the key risk signals are accurately, consistently summarized in a single easy to review location that includes annotation to source documents making validation efficient. Departing claim professionals may leave behind a very clear, complete, and accurate summary of risk signals in every pending file they were handling but frequently there are concerns making existing summaries in transferred files unreliable or at least subject to confirmation by the newly assigned professional.

These challenges to knowledge transfer in a pending inventory frequently result in delays, can contribute to missed opportunities to resolve a claim, are often the source of customer frustration and can lead to a dissatisfied workforce. AI and NLP can provide complete, consistent, and accurate summaries of the key risk signals in every file. While not a substitution for the need to review a transferred file, it makes review of a file more efficient and reduces the associated risk.

Knowledge Transfer Risk Related to Retirement

When experience talent retires skill developed over years also depart. The knowledge of what to look for, how to evaluate the risk signals and what actions to take when those signals are present is acquired through training, years of mentorship and on the job experience. Only a small percentage of learning is gained and retained through formal training programs. Most learning occurs through on the job experience guided by those who have done it before. This on-the job knowledge transfer method can work well if a workforce is well balanced with a wide range of experience levels. More experienced talent has the time to mentor and newer talent the time to absorb the key leaning. 

However, an industry faced with a high level of retirement does not have the time it takes to transfer knowledge through on-the-job experience. Therefore, the insurance industry faces a risk that new talent will not be as proficient as experienced professionals in identifying the key risk signals, recognizing the impact those signals can have on outcomes or knowing what to do to mitigate those risks. One solution is to increase the number of managers with proper experience and rely on the managers to take a more active role in file review, evaluation, and direction.

An alternative is to utilize well-AI and NLP delivered through a well-designed end user interface to act as a co-pilot or guide to frontline claim professionals thereby reducing the knowledge transfer risk related to retirement.  AI can find the risk signals that matter, it can evaluate the impact of those signals based upon industry data coupled with the experience gathered by the soon to be retired talent and make recommendations to claim professionals who are still learning a new industry, line of business, jurisdiction or claim type.

Technology can mitigate the risks in the insurance industry related to knowledge transfer based upon turnover and retirement. DocLens can show you how.





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