What Analytics Should I Expect From My Insurance Broker?

What Analytics Should I Expect From My Insurance Broker?
Feb
28
Fri

Insurance Broker & Carrier Analytics Access Introduction

The type, depth, and breadth of analytics that clients can expect to receive back from their insurance broker and insurance provider (in addition rate adjustments explanations, etc.) depends largely on the size of the client’s company, whether the client is fully insured or partially self-insured, the policies of the involved provider, and market trends. Depending on your relationship and structure, this can be frustrating for a number of senior executives.

Analytics clearly play a crucial role in the insurance business, from actuarial tables to market trends that can lead to rate adjustments for existing policies, but what analytics can you as a client reasonably expect to be made available to you?

As with most aspects of commercial insurance and even individual insurance, the answer to this question largely depends on a number of factors that can yield greatly varying results.

 

How Are Analytics Used and Why Are They Important?

In the insurance industry, analytics play a role of utmost importance in determining how rates are set and how they are adjusted over time as more information is gathered about the insurance needs and usage or individual companies, as well as developing trends of the markets as a whole.

One of the most significant and beneficial functions of a good insurance broker is using the available data and analytics in annual negotiations with the insurance carrier in order to keep rates increases lower (or bring them down) for you, the client.

  • For example, it’s common in a given year for a company to exceed the expected claims requests, at which point the carrier will typically increase the insurance rates to incorporate the new data showing that the previous year’s projections were too low. A good insurance broker, however, may be able to show that the cause of the increased claims were non-recurring events that do not need to be factored into long-term rate increases and thereby negotiate the rate increases down, if not eliminating them all together.

  • Similarly, if a broker sees rate increases coming down the pipeline from the carrier, he may be able to identify a specific cause that can be remedied with an adjustment in the policy terms instead of an adjustment through rate increases. For example, if the cause of the proposed rate increase is what appears to be a trend of emergency room visit abuse among the employee population that is driving up costs, the broker may be able to propose policy revisions that include a higher deductible or copay for such visits which can help curb the abusive behavior, thereby removing the need for a rate increase across the board.

 

Primary Factors That Determine Client Access to Analytics

There are 3 main factors that will determine whether or not you, as a client, will be privy to the analytics that are used to set and adjust your insurance rates, and what level of access you may be able to reasonably expect.

Additionally, it’s important to be aware of the distinction between the raw numbers upon which the analytics are based vs. the process through which those analytics are computed, both of which may allow for different levels of access.

  • Company Size: Possibly the main factor that will influence whether or not clients are able to access the data and analytics underlying their insurance policy rates and adjustments is the size of the client company itself. Companies with over 1,000 employees, for example, have a significantly greater likelihood of having access to such data. At first glance, this may seem merely a result of the power dynamic at play with larger companies holding more influence when requesting that data, but the greater effect of a large employee population is adding statistical significance to the data analytics that can’t be established among smaller employee populations.

    • If your company has fewer than 100 or 200 employees, expect little to no data sharing whatsoever, primarily for the above stated reason that there is limited mathematical significance to the data for such a small employee pool. In such cases, carriers make rate and adjustment calculations based largely on market trends or what’s known as ‘blended credibility,’ which is a combination of market trends and credibility tables (a type of actuarial table created based on your company’s specific data).

  • Proprietary Analytical Methods: Whereas employee population size is a relevant factor because of math, the proprietary nature (or lack thereof) for any given broker and insurance carrier is of course imposed by the insurers on their own behalf. Such limitations on access to information is usually justified in order to protect trade secrets and other information that the insurance company believes is necessary to keep private in order to maintain a competitive advantage in the marketplace or for other strategic reasons. If access to this kind of data and analytical processing is important to your company, the presence or absence of such proprietary limitations is something that should be addressed as early as possible in the process of vetting new insurance brokers and insurance carriers.

  • Fully Insured vs. Wholly or Partially Self-Insured: The breadth of involvement your company has with a single insurance broker or carrier can also be a relevant factor as to whether or not your company will be able to access data and analytics. At the risk of overstating the obvious, the more data that a given broker or carrier is able to collect on your company, the more likely they will be to be able to provide statistically significant feedback on that data, which in turn makes that data more likely to be shared than data from pools that are too small to be independently valuable. For example, even if a company has more than 1,000 employees – if that company is self-insured and/or only seeks outside catastrophic insurance from a traditional carrier, then that carrier is unlikely to be able to collect enough data to properly analyze that data on a per-company level. Therefore, the carrier is less likely to be able to share that data with the company. Of course, in cases of self-insurance, the company in question already has access to their own data, making the issue moot.

 

Client Insurance Analytics and Data Access Conclusion

As with almost all aspects of acquiring or changing commercial insurance, the best time to address issues of analytics and data sharing is as early as possible in the process of vetting potential carriers and business insurance brokers.

Whether or not you will be able to access the data relevant to your company and the methods by which that data will be used to set and adjust your rates (and whether or not such access is important to you) are decisions that need to be made on a case-by-case basis. These decisions are typically made in conjunction with a trusted advisor or broker who can take into account the specific needs of your company.

For help finding such a broker, advisor or employee benefits consultant to assist in evaluating your company’s needs, search Mployer Advisor. Our database shows ratings, areas of expertise, employer reviews and more, making it easy for you to search for and compare top-rated brokers in your area.

Mployer Advisor's goal is to add transparency to the insurance brokerage industry and highlight top performers to ultimately benefit you, the employer, and your employees.

 


About Mployer Advisor

At Mployer Advisor, our focus is creating transparency in the insurance and insurance broker, consultant and advisor space to the advantage of the employer. Analytics is our core and we will bring to light new information, tools and resources to aid employers in making more cost-effective decisions. As a phase I, we are here to help employers find the right broker or consultant and the right insurance company for them. Giving choice and initial transparency is a first step in creating an employer centric insurance marketplace.