It was the ARM Partners Conference in New Orleans on April 18, 2012. I was to speak on change. The attendees, many with bloodshot eyes, were slowly filling the room. The program was the first of the morning. Slow and bloodshot are part of the culture of early a.m. in The Big Easy.
I placed a trash can in front of the group and a bottle of baby aspirins on the podium. I explained that “my intention today is to create chest pains, because chest pains change behavior. If the chest pains get too serious, take a baby aspirin and place it under your tongue. If I upset your already queasy stomach, you can throw up in the garbage can.”
Nervous laughter followed.
An early slide included two quotes. The first: “Fat, dumb, and happy, commercial banks are being quickly replaced as financial intermediaries.” (Time magazine, June 28, 1993, Bernard Baurnohl). Agencies, not just bankers, needed that warning. The second quote was from Peter Drucker: “Whom the gods wish to destroy, they send 40 years of success.” That one was because recurring revenue from renewals makes many agents too comfortable.
As John F. Kennedy said, "There are risks and costs to action. But they are far less than the long-range risks of comfortable inaction.”
See also: Are You a Manager or a Leader?
How would your agency look if your marketing and sales were audited to see how well you were taking advantage of your opportunities? Is your organization about performance, sales, marketing, customer intimacy OR the daily transactions and the comfort of your staff and yourself?
Auditors are tough: One with the Centers for Disease Control and Prevention in Atlanta said, “When the war is over, the auditor steps onto the battlefield and bayonets the survivors.”
Is your agency and your team bruised and bloodied from battles of yesterday or up and running forward into the future? Will the marketplace, the ultimate arbiter of success, bayonet you or reward you? Are you the past or the future?
Max DePree says, “The first role of the leader is to define reality.” The following questions may help you begin to define your starting point for tomorrow:
- Do you and your team share understanding of and commit to the vision, values, mission and objectives established for your future? Will each of you and all of you be accountable for your performance and results? Are these your X commandments or X suggestions? Are these right for the world as it is and as it will be?
- Is the marketplace you serve or hope to serve in decline, level or in ascendancy? If your answer is in decline or “flat lining,” can you find new products to offer your existing clients? Can you offer your existing products to new clients or, even better, can you offer new products (services) to new clients?
- Is your team compatible with the market niches you serve? If you are blessed with some really experienced and wise baby boomers, will they be right for the Gen X and Gen Y that is your tomorrow? Will your English-speaking producers be right for a Laotian population? Will your clients shop producers based on their knowledge or their cultural/gender compatibility?
- How will you sell in a non-verbal world? Is your delivery process (sales and service) of choice the preference of your clients and prospective clients? Are they comfortable with what and how you do business? Are you comfortable with what and how they want the relationship to be? CAN YOU ADAPT TO THEIR FUTURE?
- What products, important today, might not be available tomorrow for you to sell? Is the National Flood Insurance Program sustainable, for instance, or will its vulnerability to adverse selection ultimately cause it to collapse? Will auto liability coverage be needed with self-driving cars? Will Gen Ys prefer private ownership of cars or Uber or public transportation? Will they have the appetite for home ownership that we had? Will your community survive? Will coastal properties be readily available, or will global warming have moved them all off of the coast?
- What new opportunities might be available to you that are not in your "briefcase" today?
- Will the advances in technology allow you to do more with your clients and prospects more efficiently/effectively? In a virtual world, might 7.5% commission be adequate where today you are blessed with 12%? Who will dictate commission levels in the future – you or your clients? Will carriers determine your commissions on what you need or what the market is willing to pay? Could you sell effectively with full disclosure of commission or quotes net of commission?
- What will the world of retail - malls and Main Street -- be like tomorrow? Will all the action be on the banks of the Amazon?
- Will the government finally move to a single payer healthcare system? Will your local doctors now satisfy their needs through their network versus as individual business owners? Will they be entrepreneurs or employees? Will they be in the business of business and the business of medicine, or will they specialize in only medicine?
- In the future must you be “too big to fail,” or will you be too small to succeed?
So while crude groupings may be statistically sound, Phase 2 might penalize low-risk men by tarring all men with the same brush.
The second problem is that—even if the groups don’t represent protected classes—responsible members of the group are still made to pay more (per unit of risk) than their less responsible compatriots. That’s what happens when you impose a uniform rate on a nonuniform group. As we saw, this is the textbook definition of unfair discrimination, which we tolerate as a necessary evil, born of practical limitations' But the practical limitations of yesteryear are crumbling, and there's a four-letter word for a "necessary evil" that is no longer necessary...
Which brings us to Phase 3.
Insurance Can Tame AI
It's encouraging to know that Phase 3 has the potential to make insurance fairer, but how can we audit the algorithm to ensure it actually lives up to this promise? There's been some progress toward "explainability" in machine learning, but, without true transparency into that black box, how are we to assess the impartiality of its outputs?
By their outcomes.
But we must tread gingerly and check our intuitions at the door. It's tempting to say that an algorithm that charges women more than men, or black people more than white people, or Jews more than gentiles is discriminating unfairly. That's the obvious conclusion, the traditional one, and—in Phase 3—it's likely to be the wrong one.
Let's say that I am Jewish (I am) and that part of my tradition involves lighting a bunch of candles throughout the year (it does). In our home, we light candles every Friday night and every holiday eve, and we'll burn through about 200 candles over the eight nights of Hanukkah. It would not be surprising if I, and others like me, represented a higher risk of fire than the national average. So, if the AI charges Jews, on average, more than non-Jews for fire insurance, is that unfairly discriminatory?
It depends.
It would definitely be a problem if being Jewish, per se, resulted in higher premiums whether or not you’re the candle-lighting kind of Jew. Not all Jews are avid candle lighters, and an algorithm that treats all Jews like the "average Jew," would be despicable. That, though, is a Phase 2 problem.
A Phase 3 algorithm that identifies people’s proclivity for candle lighting, and charges them more for the risk that this penchant actually represents, is entirely fair. The fact that such a fondness for candles is unevenly distributed in the population, and more highly concentrated among Jews, means that, on average, Jews will pay more. It does not mean that people are charged more for being Jewish.
It's hard to overstate the importance of this distinction. All cows have four legs, but not all things with four legs are cows.
The upshot is that the mere fact that an algorithm charges Jews—or women, or black people—more on average does not render it unfairly discriminatory. Phase 3 doesn't do averages. In common with Dr. Martin Luther King, we dream of living in a world where we are judged by the content of our character. We want to be assessed as individuals, not by reference to our racial, gender or religious markers. If the AI is treating us all this way, as humans, then it is being fair. If I'm charged more for my candle-lighting habit, that's as it should be, even if the behavior I’m being charged for is disproportionately common among Jews. The AI is responding to my fondness for candles (which is a real risk factor), not to my tribal affiliation (which is not).
So if differential pricing isn't proof of unfair pricing, what is? What outcome is the telltale sign of unfair discrimination in Phase 3?
Differential loss ratios.
The "pure loss ratio" is the ratio of the dollars paid out in claims by the insurance company, to the dollars it collects in premiums. If an insurance company charges all customers a rate proportionate to the risk they pose, this ratio should be constant across their customer base. We'd expect to see fluctuations among individuals, sure, but once we aggregate people into sizable groupings—say by gender, ethnicity or religion—the law of large numbers should kick in, and we should see a consistent loss ratio across such cohorts. If that's the case, that would suggest that even if certain groups—on average—are paying more, these higher rates are fair, because they represent commensurately higher claim payouts. A system is fair—by law—if each of us is paying in direct proportion to the risk we represent.
This is what the proposed Uniform Loss Ratio (ULR) test, tests. It puts insurance in the enviable position of being able to keep AI honest with a simple, objective and easily administered test.
It is possible, of course, for an insurance company to charge a fair premium but then have a bias when it comes to paying claims. The beauty of the ULR test is that such a bias would be readily exposed. Simply put, if certain groups have a lower loss ratio than the population at large, that would signal that they are being treated unfairly. Their rates are too high, relative to the payout they are receiving.
ULR helps us overcome another major concern with AI. Even though machines do not have inherent biases, they can inherit biases. Imagine that the machine finds that people who are arrested are also more likely to be robbed. I have no idea whether this is the case, but it wouldn't be a shocking discovery. Prior run-ins with the police would, in this hypothetical, become a legitimate factor in assessing property-insurance premiums. So far, so objective.
The problem arises if some of the arresting officers are themselves biased, leading—for example—to an elevated rate of black people being arrested for no good reason. If that were the case, the rating algorithm would inherit the humans' racial bias: A person wouldn't pay more insurance premiums for being black, per se, but the person would pay more for being arrested—and the likelihood of that happening would be heightened for black people.
While my example is hypothetical, the problem is very real. Worried about AI-inherited biases, many people are understandably sounding the retreat. The better response, though, is to sound the advance.
You see, machines can overcome the biases that contaminate their training data if they can continuously calibrate their algorithms against unbiased data. In insurance, ULR provides such a true north. Applying the ULR test, the AI would quickly determine that having been arrested isn’t equally predictive of claims across the population. As data accumulate, the "been arrested" group would subdivide, because the AI would detect that for certain people being arrested is less predictive of future claims than it is for others. The algorithm would self-correct, adjusting the weighting of this datum to compensate for human bias.
(When a system is accused of bias, the go-to defense runs something like: "But we don't even collect information on gender, race, religion or sexual preference." Such indignation is doubly misplaced. For one, as we've seen, systems can be prejudiced without direct knowledge of these factors. For another, the best way for ULR-calibrated-systems to neutralize bias is to actually know these factors.)
Bottom line: Problems that arise while using five factors aren't multiplied by millions of bits of data—the problems are divided by them.
The Machines Are Coming. Look Busy.
Phase 3 doesn't exist yet, but it's a future we should embrace and prepare for. That requires insurance companies to redesign their customer journey to be entirely digital and reconstitute their systems and processes on an AI substrate. In many jurisdictions, how insurance pricing is regulated also must be rethought. Adopting the ULR test would be a big step forward. In Europe, the regulatory framework could become Phase-3-ready with minor tweaks. In the U.S., filing rates in a simple and static multiplication chart for human review doesn't scale as we move from Phase 2 to 3. At a minimum, regulators should allow these lookup-tables to include a column for a black box "risk factor." The ULR test would ensure these never cause more harm than good, while this additional pricing factor would enable emerging technologies to benefit insurers and insureds alike.
Nice to Meet You
When we meet someone for the first time, we tend to lump them with others with whom they share surface similarities. It's human nature, and it can be unfair. Once we learn more about that individual, superficial judgments should give way to a merits-based assessment. It's a welcome progression, and it's powered by intelligence and data.
What intelligence and data have done for humanity throughout our history, artificial intelligence and big data can start to do for the insurance industry. This is not only increasingly possible as a matter of technology, it is also desirable as a matter of policy. Furthermore, as the change will represent a huge competitive advantage, it is also largely inevitable. Those who fail to embrace the precision underwriting and pricing of Phase 3 will ultimately be adversely selected out of business.
Insurance is the business of assessing risks, and pricing policies to match. As no two people are entirely alike, that means treating different people differently. For the first time in history, we’re on the cusp of being able to do precisely that.
