Auto Insurance Risk Selection – The Good, the Bad, and the Ugly
Many auto insurance underwriters aim to separate good, bad, and ugly insurance risks. Theoretically, good risks are safe drivers, bad risks are dangerous drivers, and ugly risks are fraudulent applicants.
However, it may be time to rethink this paradigm. It’s quite possible that the only bad risk is a fraudulent applicant. Everyone else is simply adequately or inadequately rated.
Why “Good vs. Bad” Doesn’t Always Make Sense
Take stocks as an example. Let’s say you have $10,000 to invest and you are trying to decide which stocks to buy. Is a stock valued at $1 a share a good stock and one valued at $100 a share a bad stock? Or is it the opposite? It’s impossible to know because price alone doesn’t render a stock good or bad.
Whether it’s movie characters or real-life decisions, people love to break the world down into good versus bad. It’s simple, it’s clear – and it’s often wrong.
To determine whether a stock is a smart buy, you first need to compare the current price to the price target. If Stock A has a current price of $1 and analysts expect it to reach $5 to $10 in the next year, it could be a smart investment. If Stock B also has a price target of $5 to $10 but its current price is $9, it might not be a smart investment. In fact, you’re more likely to lose money than you are to gain money, even though the target prices are the same.
In other words, it’s all relative.
Auto Insurance Risk Selection Is Relative, Too
A driver with three speeding tickets might seem bad if the insurer is trying to separate risks into categories, but the reality is much more complex. Before making an assessment, the insurer needs to compare two numbers – the rate to be charged and the losses likely to be generated.
Let’s say two drivers, Aaron and Brad, have the same likelihood of a crash due to a tendency for speeding. Aaron is charged a low rate that doesn’t adequately cover the risk, whereas Brad is charged a higher rate that does adequately cover the risk. The risk level is the same, but Aaron’s policy is likely to cost the insurer money (inadequately rated), whereas Brad’s policy is likely to make the insurer money (adequately rated).
As long as the rate is adequate for the likelihood of a claim, the risk is acceptable. Put another way, a driver who speeds is only an unprofitable risk if the rate charged isn’t high enough.
Why This Perspective Is Game Changing
Avoiding “good versus bad” language may seem like splitting hairs, but it actually represents a fundamentally different way of assessing risk.
When insurers embrace the idea of adequately- versus inadequately-rated risks, they no longer need to turn off entire segments. Instead, they can leave all segments turned on and select adequately-rated risks one policy at a time. Policy-level risk selection enables insurers to quote more business, earn more premiums and grow their businesses.
The Time for a Paradigm Shift Is Now
The auto insurance sector is primed for a paradigm shift for a few reasons.
For one thing, profitability is down. Insurers are looking for new ways to drive down their loss ratio and boost their bottom line.
For another, the technology finally exists. Recent breakthroughs in machine learning have paved the way for precision auto policy risk selection using an algorithm based on your company’s policy and claims data.
In Just Four Months
Carriers and MGAs of all sizes can leverage the Soteris solution to achieve more accurate, policy-level risk selection. Soteris SaaS runs alongside your existing systems with existing rate filings, enabling speedy implementation in as little as four months.
