Which direction is the right direction when it comes to agency technology?
Dive into the details, and the short answer is different from agency to agency. Every agency has a slightly different focus. When you deal with tactics and short-term solutions, you can go in a multitude of directions. When you shift your mindset into thinking strategically, the multitude of choices tends to collapse into a single path. The key: Make sure the path is actually leading you where you want to go.
When you look at the strategic direction of automation and technology—both inside and outside our industry—the correct path is clear. Technology is no longer a solution to agency growth. Buying an agency system, filling your offices with servers and building an infrastructure isn’t enough. In fact, there are now better ways to deal with commoditized IT.
Let’s play a game of “Jeopardy.” The answer is: It’s the data. What’s the question? It doesn’t matter. Going forward, the answer will be the same: “It’s the data.” The game has changed. It’s time to change the way we play.
What are brokers capable of, and what should they do with insured data? The answer is pretty clear to me: Use it to provide insight for the insured. But many brokers have a different philosophy on what role the broker plays.
Are we truly just an insurance product distribution arm? This is how many carriers see us. Is your firm an entire organization of producers whose role is to place premium? Or are we a trusted partner to the insured whose primary role is to help them understand and mitigate risk? These are two completely different types of organizations, both with a different view of the answer to the data question.
Focused on being a trusted partner to your clients? Then you must invest in data analysis. The role of insurance placement expert is valuable, but it needs to be enhanced with a deeper knowledge of the individual insured. The world is moving in this direction and we must adjust to keep up. From our personal lives to our businesses, we are being groomed to expect individual answers and insight from our chosen vendors and partners.
If your firm’s primary goal is to place premium, it doesn’t make much sense to invest time, energy and capital gearing up your data machinery. It’s much wiser to invest in your processing machinery. Create a factory model. Secure a client. Place premium. Service. Renew.
Why not just wash, rinse and repeat?
But if this is your goal, your agency likely won’t be around in 20 years. Carriers have more capital, more drive and more reason to invest in transactional systems that make the independent agency increasingly irrelevant. The way insurance coverage is placed is changing, and the model doesn’t favor the “placement-only” agency.
Many brokers have heard this warning before, but what does this really mean? As brokers we don’t usually dive too deeply into the machinery that defines premium and potential profitability of risk. As agents of the insured, we push hard to keep premium down to a level that keeps our clients happy. As agents of the carriers, we work to lower insured risk, which prevents claims and ensures profitability. When both parties are happy, we have a recipe for a successful agency. As data collection and analysis technologies improve, carriers are envisioning a future that has the potential to completely change the way they evaluate and price risk. What’s the broker angle? It will only work if the carriers can strengthen their relationship with the individual insured. That’s our turf. As brokers, we can participate in this model, or we can sit on the sidelines while the game changes.
Peering Over the Fence
You probably already have a good idea how risk evaluation works. Under the hood, the process is pretty basic. Gather data points related to the risk, group the risk with similar risks and calculate the probability and severity of loss. The data points could be based on geography, industry, historical records and any number of other nearly endless metrics. By grouping similar risks, carriers are able to continually refine risk probability. In addition, grouping risks of a certain class allows carriers to manage risk at an aggregate level. For a certain class of business, your chances of managing a profitable portfolio are increased by pooling together like risks that all share a similar probability of loss. If there’s a 5% probability of a loss for each of 100 risks, there’s a good chance 95 of them will have no loss. It’s a simple game of playing the odds. Get the numbers right, and the profits will outweigh the losses.
So what does this model mean for an insured who exercises impeccably safe business practices or an individual who is genetically predisposed to avoiding disease? The current pooling model provides no way to effectively identify these inherently low-risk insureds from the pool of medium-risk insureds. So these high-value insureds—high value because of the low probability of a claim—are effectively penalized by the pool. In the past, pricing risk on an individual basis was too risky a proposition because the volume of data on individuals was simply not available. But this is changing.
On the Horizon
With the advent of big data and the large-scale aggregation of information by data brokers, we are rapidly approaching a world where every piece of data on every individual and company will be available to anyone who chooses to participate in the data game. Carriers have already taken the first step of getting a handle on their internal data. From here it’s a matter of tapping into big data stores and adjusting the actuarial machinery to process the risk based on the new data. The end result? Individualized rating based on a pool of one. This concept has the potential to decimate the historical method of determining premium and profitability.
When carriers can access the “digital you” of everyone and everything, they will be closer to the individual insured than the insureds are to themselves. I’ll say it again: That’s our turf. We have the relationship with the insured now. We have the data now. But we don’t have a handle on it, and we’re only just beginning to understand its inherent value.
If we want to retain our position, we need to get our arms around the data and start looking through a new lens.