Betting Against the Future: Analytics and Insurance Talent Management

Betting Against the Future: Analytics and Insurance Talent Management

Human resources professionals in the insurance industry face a unique set of challenges from both technology and talent. Analytics have made a splash in many industries, but their direct application to insurance HR is not always readily apparent.

Turning your HR operations into a data-driven powerhouse requires convincing management that the investment is worth it. You can note that leading talent management and enterprise resource planning systems offer a bevy of metrics and options to select key performance indicators. But, these often won’t help you apply metrics, KPIs, or global data to your workforce.

You need quick, small wins that address some of the most significant problems you face. This quick dive into predictive and workforce analytics can help you learn where applications may exist and how to think about data for your organization.

Measuring Knowledge Transfer

As an HR professional in the insurance market, you know the industry faces a workforce gap. For many of you, it’s growing.

The aging and retiring workforce has pushed out middle management in almost every industry. The Millennial workforce focuses on digital skills and insurance, as an industry, has struggled to give itself a technologically savvy appearance. Today’s insurance workforce is seeing the most skilled practitioners retire and struggling to pass on lessons to a small set of younger workers that lack significant industry experience.

The transfer of knowledge across the divide is essentially for long-term health of each insurer. If you’re not tracking the success of employees during and after the knowledge transfer, then you might be wasting your employees’ time and harming your company’s long-term viability.

Your analytics should extend to track the formal and informal mentoring that goes on in the workplace. This can be tracked via established programs and with simple questions in a weekly review or check-in that ask: Who was the most helpful in the office this week? Did anyone show you something you didn’t know? What was it?

Onboarding programs already require significant interaction between new employees and your top staff. However, these are often limited to checking off a box so that you know everyone has practiced with your software and read the harassment policy. Following through with talent tracking that knows who demonstrated the software may help you find bottlenecks.

If Dave just gives your employees the handout but Michelle walks them through each process, there’s a potential that Michelle’s trainees will perform better over time when training on complex tasks. However, Dave may achieve the same results when it comes to understanding and adhering to corporate policy because nothing beyond reading the policy is needed.

Michelle’s hands-on approach can then be reserved for training on complex systems where gains are largest. If she enjoys mentoring or the process being reviewed, you might see even higher gains in bridging the skills gap.

Once you establish a baseline of this data, predictive analytics can help you find similar benefits in your knowledge transfer as well as many other KPIs.

Considering KPIs: The Average Time to Settle a Claim

One of the top KPIs across almost all insurers is the time it takes for the company to settle a claim. This data can be tracked across touch points to see how long each person takes to complete their part of the settlement process, as well as tracked separately for each policy the insurer offers.

Policies all have different claims periods so it’s not uncommon to see different a large gap between the speed of closing claims across products. No analytics program will deliver a paradigm shift in claims processing that resolves medical claims faster than a theft.

However, analytics may help you optimize the claims process across your products.

Breaking down the entire process and tracking each individual element may show you that claims in a certain region take longer to have an inspector visit, or a specific hospital may take five calls to get a document compared to your average of three.

Applying predictive analytics to this process can help you best identify partners or employee traits that make someone right-fit for a particular position.

You may also discover common elements that have no negative impact, but highlight areas for improvement. A medical insurer may find that new doctors’ offices are using a certain type of EMR/EHR, and a software improvement on the side of the insurer can yield proper integration so those records auto-populate claims forms.

This is just one KPI, but tracking its data can help insurers realize operational efficiencies that have positive benefits throughout an entire operation.

Consider the EMR/EHR example. Predictive analytics may show that you can expect a certain volume of claims with offices using a specific format. It may also tell you that you can expect a quicker and cheaper claims resolution process after you integrate to use that format. Underwriters now have a reason to offer a small discount for working with integrated partners, using claim resolution data to create a preference for products that will have shorter resolutions in the future.

Taking the Next Steps in Workforce Optimization

Pairing analytics with performance reviews also gives you a chance to ask one of the most important questions for top performers: What aspect of the work do you like the most?

Review this information regularly through workforce management practices like templated performance improvement plans. A steady stream of data will allow HR professionals to track these desired work areas and identify opportunities for top performers and rising stars to experience more of the work they enjoy.

We know that happy workers tend to perform better. The University of Warwick quantified that last year with a study[i] that found happy workers are 12% more productive, while unhappy workers are 10% less productive.

Applying this via analytics and PIP templates is pretty straightforward and won’t impose on any HR team. Review performance data to identify the areas where each employee perform best. Add a question to the PIP template that asks them what work they most enjoy or where they feel underutilized. Review responses and build a list matching your star employees with their desired work areas.

When new opportunities arise in one area, match it with the top employee for that work area. If this creates a gap in another work area, match the top employee for that process, and so on.

Specific to insurance, this may move your team around where certain members interact with vendors while others are your new front-line when claims are first made. Employees who have the best relationships with your inspectors or adjusters may be able to leverage that into improved performance and operational efficiency.

Analytics provides that information that your knowledgeable HR team can use to identify gaps. Modern software and talent management best practices prepare them to capitalize on each opportunity

Don’t forget to check out BPI’s TalentBall with the Texas Rangers at Globe Life Stadium this coming April 28-29 to help forward thinking companies like Cigna, Humana, QBE, Torchmark and others to use analytics to make better talent decisions. Visit the Talentball website at: http://talentball.bestpracticeinstitute.org.

 

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