For years, we’ve been hearing about the impending talent crisis in the insurance industry. Statistics indicate that a significant number of experienced insurance professionals are nearing retirement, and with industry unemployment low (1.7 percent), replacing them won’t be easy.
At a recent conference, attendees asserted that they’d already been experiencing the effects of this brain drain, particularly in the claims business. Simply put, they’re struggling to get their inexperienced adjusters up to speed and to help senior staff improve efficiency. And that’s where analytics comes in—it helps neutralize the knowledge gap.
The advantages of analytics are undeniable. Organizations using analytics are twice as likely to be top-quartile financial performers, and five times more likely to make decisions faster than their competition, according to a Bain study.
Most insurers recognize the value of analytics. Here’s the challenge: How do you best incorporate analytics into your organization? For that, you need a solid plan in place before you roll out an analytics platform.
Understanding the problem and creating objectives
Advanced analytics can seem daunting. Concepts such as machine learning and predictive modeling sound complex and nuanced, and it’s easy to get lost in the technical minutiae. Instead of focusing on the mechanics of implementing analytics, insurers should first clearly define the business problem they want to solve and their plan for measuring success.
In workers’ compensation, there are several issues that contribute to claims challenges: inexperienced adjusters, untimely claim reporting, treatment delays, poor initial diagnosis, comorbidities, drug dependency, and more. Understanding these challenges and how they impact the claims process is the critical first step. It involves reviewing metrics, key performance indicators, and audit results, and interviewing claims staff to identify areas of opportunity.
Once you understand the problem, you can define your objectives. For example, objectives for workers’ comp analytics could include:
- triaging claims to the appropriate unit as soon as possible
- preventing high-severity claims from slipping through the cracks
- identifying claims with opioid use and multiple fills
- resolving claims with low severity potential more efficiently
With your objectives in place, you have a better understanding of how to proceed with a plan.
Establishing an effective implementation process
The next step is establishing a process for implementing data analytics. This requires developing both project management and change management plans. The two complement each other. Project management ensures that the analytics platform is properly implemented on time. Change management ensures that it’s embraced, adopted, and used.
While most organizations emphasize project management, many neglect to implement a thoughtful change management plan. This involves establishing procedures for preparing, training, and supporting teams in the claims organization. Carriers should also consider how analytics fits into their existing process and how it affects their current workflows.
Measuring success and refining plans
Once the objectives and process are established, it’s time to determine how to measure the success of the analytics platform. There’s no way to know whether the solution is successful if there is no baseline to measure it against.
Consider the objectives previously mentioned. Some measurements of success could include the:
- time it takes to assign a claim to the correct handling unit
- number of transfers before a claim is assigned to the final handling unit
- volume of nurse referrals
- time it takes to involve nurses
- time to final reserve
- cycle time for low-severity cases
Establishing these measurements prior to implementing a solution is critical to the success of an analytics program. It’s also important to establish a cadence for reviewing results, so you can refine your plans and define new objectives, if necessary.
Better insights for better claim outcomes
Data is ubiquitous in workers’ comp, so insurers are wise to find ways to use it to their advantage. There are myriad ways analytics can improve the claims process—for instance, by identifying high-severity claims early, promoting consistency in claim handling, and improving return-to-work results for injured workers.
As insurance workforce demographics change, it’s more important than ever to use data to ensure consistency and efficiency in decision-making. Proper planning can go a long way toward effectively operationalizing an analytics platform.
ISO Claims Partners offers predictive analytic solutions for workers’ compensation and liability claims that inform decision-making and help improve efficiencies. For more information, contact me by email at firstname.lastname@example.org or by phone at 978-825-6012.
 Insurance Industry Facing Competitive Labor Market,” Insurance Journal, April 1, 2019, https://www.insurancejournal.com/magazines/mag-features/2019/04/01/521800.htm.
 Travis Pearson and Rasmus Wegener, Big Data: The Organizational Challenge, Bain & Company, 2013. See: https://www.bain.com/contentassets/25c167a5149c42168994338f9dc99ffe/bain_brief_big_data_the_organizational_challenge.pdf