I find the insurance industry's use of analytical data fascinating--and not always in a good way. Often the use and analysis is a complete clash with the absolute refusal to use math analytics. The preference is the use of emotional analytics. These two methods have been fighting it out forever. Usually math loses to emotion on the edges and occasionally at the center until something falls completely apart.
For example, early in my career at an insurance company, a battle royal raged between the home office actuaries claiming a double digit rate increase was required versus field marketing advising such an increase was suicide. If the actuaries were right, less than the recommended rate increase would lead to a slow death. If marketing was correct, a large rate increase would lead to a fast death. Both roads led to extinction so I decided I did not want to work there long--and indeed the company disappeared a few years later. Not from that specific situation, which was only a symptom of the larger disease. No one in the company seemed capable of analyzing how much rate was needed for healthy growth.
I have heard and witnessed many situations of C-suite executives preventing actuarial rate indications from being filed. The C-suites were proud of their decision because (when it worked) they achieved the holy grail of higher growth and decent profit in spite of their actuaries. This leads to a side question of whether those specific actuaries were any good or if actuaries are really necessary. One CFO recently told me the best way for the company to decrease expenses was to eliminate the actuarial department because no one had any faith in their results anyway.
This disregard of math analytics is an issue from another perspective where the C-suite just trusts the analytical software being purchased for various forms of predictive modeling without implementing adequate and continuous testing controls. The industry is already beginning to develop black eyes as a result of the ludicrous lack of responsibility associated with these analytic programs. Just one example I have seen in the last six months is where a long-time personal lines auto customer of a carrier realizes the death of the husband. The wife never had the credit in her name so upon his passing, the credit score defaults to her and she has a low credit score. Her rate increases dramatically. Absolutely, under no circumstances imaginable, does a legitimate basis exist for that rate increase. But companies refusing to actually understand how their own rates work and putting proper parameters around them leads to a quick loss of reputation by their agents, consumers, and eventually regulators. I listened to an interview with an insurance commissioner who expressed angst with companies who tell his department they can't explain these models. They just "trust" the numbers are right.
Another factor is the people using the data have no analytic/statistical background. For some reason they think everything depends on averages and quartiles. This miscue is especially prevalent with agencies and agency consulting firms. Certain universes do work on a normal curve (averages and quartiles are dependent on the universe fitting a normal curve). For example, the height of all the people in the world fits a normal curve.
Producer success does not fit a normal curve so it cannot be managed successfully as if it did. Producer success probably fits a Pareto curve. Facts falling on a Pareto Curve require a far different approach than if they fall on a Normal Curve. If as a reader you do not know the difference, then to manage successfully using data, learn or turn it over to someone that does.
Another example is managing to the wrong metric. An excellent example of this with carriers is they failed to understand and articulate the difference between volume and growth relative to what they needed from their agents. They, and then their agents, used the terms "volume" and "growth" synonymously. They kept telling and telling agents they needed volume. So agents went and merged and joined clusters to generate volume. If a company needs $1,000,000 volume, four agents combined their $250,000 books and create $1,000,000 volume. Of course, the company is no better for it and in some ways, likely worse because now they are likely paying out more profit sharing.
What the company meant to emphasize was the need for growth. Volume is a dollar number. An agency has $1,000,000 volume. Growth is a percentage number. An agency has 5% or 10% growth. What companies need in this softest, calmest market is growth regardless of how much volume an agency already has. Volume is historical. "I have $5,000,000." Growth is future, "I am going to grow 7% each of the next three years." Companies need that future growth without ANY regard of the agency's current volume.
I am not sure why so many people in this industry think they know math and statistics better than they do. When this lack of knowledge is combined with the outsized importance placed on emotional analytics, it is no wonder many poor decisions are made. A few brokers and carriers have clearly identified this weakness in their peers. They are achieving alpha success.
These firms have aligned people with knowledge of math and statistics within the right decision making capacities to achieve more than their share of success. The methods they are using are often unique and sometimes so different than normal that when discovered, I've heard executives advise their methods are not moral. Very different methods are not inherently immoral but they feel so different, the emotional analytics is to reject them outright.
My recommendation is that firms need to align themselves with better educated math/statistics people whether employees and/or consultants and then restructure their firms internally to give these people a better opportunity to be heard without excessive emotional bias. That emotional analysis is still critical because if one lets the math/statistics geeks go too far, the result will not be any good either. I am advocating for a far better balanced system for your organization.
NOTE: The information provided herein is intended for educational and informational purposes only and it represents only the views of the authors. It is not a recommendation that a particular course of action be followed. Burand & Associates, LLC and Chris Burand assume, and will have, no responsibility for liability or damage which may result from the use of any of this information.