Jack Marrion

Through the years I have run hundreds of thousands of hypothetical return calculations for indexed annuities—and that is not hyperbole.

Hypothetical output, based on past index movements, can give you an idea of future returns—if there are no moving parts. That means the participation rate, caps and spread are locked in and cannot change during the 5-, 7-, 10- or 12-year surrender period of the product—and the past repeats. Sadly, the past doesn’t repeat, except in very broad sweeps with irregular timing, which means even if an annuity’s participation rate, caps and spreads are known, this hypothetical modeling cannot predict future results. The only thing this modeling does show (if you have no moving parts) is how the annuity would have performed in past periods. Plus, if a hypothetical model does not have all of the crediting parts locked in for the term it cannot even predict the past.

One problem with models in which the parts may move is that almost no one moves them. Influenced by a cognitive bias called naive extrapolation, most models take the current day’s rates, apply them to past periods, and then conclude that is how annuities would have performed. Thus, hypothetical illustrations done today use caps of 4 to 5 percent; those done a few months earlier used caps of 2 to 3 percent; and illustrations calculated a few years ago assumed caps would never, ever, be lower than 8 percent. Reality has shown how foolish it is to assume that everything will stay the same in the financial world—and that includes using a static cap.

Every modeling example I’ve seen from a carrier or agent assumes this never-changing world, but because the world does change (as do annuity caps) the output generated is meaningless garbage. Saying “let’s assume the caps from 1996 to 2001 were 3 percent”—when in reality they averaged 9 percent—means your assumption is invalid, as are your results. Sadly, even academic studies looking at annuity returns (or studies trying to appear academic) usually suffer from this bias, making their conclusions worthless.

Very few studies have attempted to adjust to the real world by looking at historic annuity pricing factors and then deciding what the index annuity rate or cap should have been. So they’ll note that in 1997 the average bond yield was “X” and the implied volatility of the index option was “Y” and conclude that the cap would have been 14 percent. This works better than assuming nothing ever changes, yet, like all past-based predictions, this assumption is handicapped by the reality of the past. What this means is although one could have done a hypothetical model using the guaranteed minimum caps and rates,  no one did because no one would have believed that 2 percent caps would ever be a reality 5, 10 or 15 years ago.

Taking into account past pricing factors does let you know what caps and rates should have been. I have copies of hypothetical illustrations that assumed participation rates of 200 to 300 percent (without caps) for certain years in the early 1980s when bond yields were in the double digits. Can you imagine telling an annuity owner “the index went up only 6 percent, but you earned 15 percent interest”? Sounds absurd, and this points to the fundamental reason why indexed annuity illustrations cannot predict the future or the past.

Unlike an indexed mutual fund or exchange traded fund where next year’s return is index performance minus a previously known fee, the degree to which an indexed annuity participates in the index return next year is determined by the annuity carrier. Although the return cap is largely based on the realities of a carrier’s investment portfolio returns and the price of hedging the cap on the annuity policy, there is a human factor. This human factor influences and may override the financial ones in setting the cap for the coming year.

Sometimes the human factor provides a more generous cap than finances dictate. I have witnessed this happening during the last couple of years at several carriers when caps were renewed at a higher level than economic factors justified. Sometimes the human factor results in a stingier cap than finances allowed, something I witnessed in the middle of the last decade. The reasons why the annuity carrier may decide to ignore their own financial pricing model are varied, but are often due to what the competition is doing. Thus, if your pricing model permits you to offer a 250 percent participation rate (that 15 percent interest mentioned earlier) and all of your competitors are paying 100 percent, you might offer 110 percent of index performance (but not 250 percent).

Does this mean that creating hypothetical illustrations of indexed annuity performance (and indexed life performance as well) is a waste of time? If you’re trying to tell someone how their annuity (or IUL policy) will perform in the future, the answer is “yes.” However, this doesn’t mean you should not do hypothetical modeling.

What the models will tell you is whether a carrier’s current rates and caps are out of whack with the rest of the field. What I mean is if you run illustrations for 10 carriers and one has an average return 3 percent higher than the others, you need to find out why. It could be that the one carrier has lower costs or that its crediting method performs better. A more likely answer is that the current cap is overly generous and will be “adjusted” over time.

What it comes down to with indexed annuities or indexed life insurance is that your return is far more dependent on how the carrier treats you when they renew those participation rates, caps and spreads than on the financial factors.

Find a carrier you trust and throw away the spreadsheets.

You’re sitting in your aisle seat and at scheduled departure time the pilot gets on the intercom and says, “Due to a ground traffic control issue our flight is delayed. We estimate we’ll depart in 40 minutes.” You are less than thrilled and grouchily settle back in your seat. After 30 minutes the pilot gets back on the air and says, “Well, we got clearance a little earlier than we thought, prepare for departure.” Compare your state of mind here with the following more common situation.

At departure time the pilot gets on the intercom and says, “We have a delay. We estimate we’ll depart in 10 more minutes.” You quietly wait while 10 minutes becomes 13, 15 and then you hear, “We’ll depart in another 10 minutes.” And again 10 minutes goes to 12 goes to 15 before you hear, “Prepare for departure.”

In both cases the plane left 30 minutes late. Yet the odds are that you are more unhappy in the second situation than in the first—and the reason is due to anchoring.

We tend to anchor around the first number we hear when used with an unfamiliar topic. If the pilot says a 40-minute delay or a 10-minute delay, we establish whatever number is stated as a standard by which we’ll judge future action.

The reality is both flights were delayed 30 minutes. However, due to our anchoring bias in the first instance we feel as if we are leaving 10 minutes early. In the second, well, let’s just say we don’t feel that we left early. In addition, the first pilot gave a reason for the delay and the second one didn’t. As we become more familiar with an unfamiliar topic—we know now that flight delays can be caused by ground traffic control issues—we are more willing to accept problems along the way.

Some insurance agents will present the lowest possible premium quote, especially if they know they’re in competition with someone, and they may win the initial sale on that basis. However, often a quote assumes a level of good health that a real-life buyer doesn’t meet, or the assumptions on cash value growth are too optimistic. Thus, the agent must return to the buyer with a higher price, having created a low price anchor in the client’s mind. The result can be the buyer backing out of the sale.

It is better for agents to use the approach of the first pilot by explaining, “Mr./Ms. Consumer, you need to know that regardless of which insurance company you use, the ultimate premium you wind up paying is largely dependent on the state of your health and the assumptions made about future policy returns. If you have some health issues and we use very conservative assumptions, the premium might be as high as $250 a month; but if your health is good and we’re more optimistic, the premium could be as low as $130 a month.”

By establishing a confidence interval for premium, a consumer is less likely to not buy if the ultimate premium is higher than the initial one discussed.

As long as a competitor’s bid comes in higher than $130, the original agent will probably get the business. In addition, because the agent has explained how the premium price is determined, a low-ball bid from a competitor will now be viewed with suspicion. The agent has essentially given the client a confidence interval, which means the anchor is $130 to $250. If the final premium turns out to be $180 a month, the buyer is more likely to stick with the purchase because it is still less than $250.

Anchoring comes into play in a different way if we are familiar with a topic. For example, let’s say you’re a frequent flier on a certain airline and your experience has been that a particular flight always leaves 30 minutes late. In such a case, what the pilot says won’t affect your anchoring position.

The same anchoring holds true in the life insurance sales process. A consumer who owns life insurance or has already been shopping around may already have preconceived notions of what the premium should be and what assumptions should be used for future returns. The agent needs to determine whether these anchors have been established and whether they are realistic.

Anchoring and the Law of Small Numbers

The media is great at using this bias. They’ll run a story about, say, a person who gave a dollar to a homeless person, bought a lottery ticket with their last dollar, and then won $10,000. They’ll then search the archives and report a story from years earlier in which a person gave a buck to a beggar and then won the lottery. The idea they are trying to convey is first, this happens a lot, and second, since two people gave to beggars and were then rewarded, there must be cause and effect.

The reality is that there is no correlation between the two incidents and there is no causality, but people tend to believe in the “law of small numbers,” humorously named because people tend to attach as much decision weight to a small data sample as a large one.

The law of small numbers and a belief in causality can be used to improve the perceived value of life insurance. A reason for not buying insurance is not thinking you’ll need it—I’m not going to die in the near future. Building an obituary book, showing obituaries of real people who died in their 30s, 40s, 50s, 60s (whatever is near the age of the prospect) brings home the point that, “Yes, you too can die and thus you need life insurance.” 

The deferred annuity guaranteed lifetime withdrawal benefit (GLWB) was invented by a variable annuity carrier in 2003. In June 2006 GLWBs were introduced into the fixed index annuity world and in a short time GLWBs were everywhere, included in a majority of annuity purchases. The reason is that the product has a good consumer story.

At its simplest, GLWB guarantees an annuity owner a specific level of withdrawals that are guaranteed to last as long as he lives—even if the cash value of the annuity goes to zero. Unlike a life immediate annuity, which also guarantees a lifetime income, the cash value of a GLWB annuity remains available to the annuity owner. Although the cash value may well be decreasing or even run out—which is the uncertainty the GLWB guards against—the annuity owner retains control.

GLWBs didn’t remain simple very long. To gain a competitive advantage, carriers guaranteed that the amount of the future guaranteed withdrawals would increase even if the cash account value did not. These “roll-up rates” were initially a modest 4 to 5 percent, but a roll-up war resulted in guaranteed compounded annual increases of 7 to 8 percent (in one case 12 percent, non-compounded). This all works for the carrier if the cash value of an account also does okay; however, if it doesn’t, an annuity owner could wind up with the carrier guaranteeing lifetime withdrawals of 10 percent or more on the actual cash account value, which did happen in a few cases and was feared would happen in others.

From the Carrier Side

The carriers’ hope is that an annuity does not run out of money and require them to make GLWB payments out of their own pockets; the way this doesn’t happen is if an annuity owner dies, earns a sufficient return, doesn’t use the benefit and/or the carrier charges a large enough fee to help offset the cost.

If a group of 65-year-olds withdrew 5 percent a year from their annuities and averaged even a 3 percent annual return, the money would last until age 96—almost all of the annuity owners would be dead before their annuities were depleted. In these cases, carriers have spent very little if any money out of their pockets to cover the lifetime guarantee.

Let’s say a carrier guaranteed a group of annuity owners that they could withdraw the equivalent of 7 percent of the cash value at age 65 (reflecting a guaranteed roll-up rate). If the annuity owners continue to earn 3 percent, the money runs out at age 84. Based on life expectancy, half of the group would be dead, but the others would still be alive with zero cash values. Thus, the carrier would have to continue to pay for the lifetime income.

But wait, if the carrier has charged fees over the years, half of the people who paid the GLWB fee died before the protection was needed. In addition, some of the annuity owners paid a fee for a few years and then decided not to use the benefit or cashed in the annuity. Thus, all of these fees, along with the fees paid by the people who are receiving lifetime income, are designed to offset the out-of-pocket expense of the carrier.

It is even better for the carrier if a annuity owner earns a high return year after year. For example, if the annuity earns 5.7 percent instead of 3 percent, the money once again lasts until age 96 and the odds are that the carrier won’t have to pay. The carriers were counting on decent returns over the long haul and priced the risk of offering the GLWB accordingly, but then came 2008 and its aftermath.

2008-2012

The 2008 market crash caused the cash value of variable annuities to drop, but not the income value of the GLWB guarantees. This caused annuity carriers great concern that annuity owners would turn on their “payout faucet” at the worst possible time—when account values were depressed—which led carriers to back down benefits on new policies. A few rash carriers even eliminated the benefit. Even though at least one study has found that annuity owners didn’t start taking benefits at the worst possible time, this has not resulted in variable annuity carriers rushing to restore benefits (Ruark Consulting, LLC, 2012 Variable Annuity Policyholder Behavior Studies).

While the crash did not cause fixed annuities to lose value, the interest rate climate of recent years has made building value more difficult, which has impacted GLWB design. Previously I said if an annuity owner withdrew 7 percent of the annuity cash value beginning at age 65 and earned 5.7 percent, the money would last until age 96 or, at 3 percent, the money would last until age 84. However, the money would also last until age 96 if an annuity owner cuts the payout from 7 to 5 percent and earns 3 percent.

One reaction to the low interest rate environment was to cut payouts or roll-up rates or roll-up accumulation periods so there would be less of a difference between cash value and GLWB promises. Another reaction was to increase fees from an average of 0.4 percent in 2008 to 0.9 percent in 2012. One innovation was to introduce a stacking method which essentially shifts part of the roll-up rate risk onto the annuity owner.

A roll-up method is “either-or”: The higher of either the account value produced by actual returns or the roll-up rate is used to compute the payout. A stacking method adds a guaranteed percent to the actual return earned and is lower than a roll-up rate, thus it lowers the risk of the carrier needing to pay out of pocket—yet the final guaranteed payout may well be higher.

Carriers reacted and, in some cases, overreacted to the gloomy realities of recent years and took back some of the benefits. However, the attitude in 2013 has become more positive and creative.

2013

One index annuity has reintroduced a benefit to the market (originally offered on the retired Sun Life SunDex annuity) that allows the owner who doesn’t take the maximum payout in a year to save the difference for future use. A few carriers have introduced an enhanced death benefit feature paying the higher of the value generated by the stacking/roll-up rate or cash account accumulated value. Recent increases in bond yields have resulted in some carriers raising their roll-up rates or increasing the number of years they may work to increase future guaranteed income. If the outlook for rising bond yields continues, we should expect to see more carriers enhancing GLWB benefits.

In many respects the GLWB is the perfect product at the perfect time because it gives retiring and soon-to-retire boomers a guaranteed lifetime income and control of their asset. Although the guaranteed increases may be less than they were in the “go-go” 2006-2007 years, they are still competitive with other income choices. In addition, if bond yields continue to improve, the benefits offered will also be enhanced.

A decade after they were introduced, GLWBs continue to be a good consumer story. 

A while back I talked about the general concept of framing. All of the data we receive is filtered by our own internal decision-making biases; the way the data is framed as it enters the fog of these biases affects our decisions. Framing isn’t good or bad, it just is. However, if the framing takes into account our internal biases when presenting data, it can result in consumers making better decisions.

Narrow Framing Works Against Life Insurance

More than 30 years ago researchers Amos Tversky and Daniel Kahneman asked a group of people to make a pair of decisions. In row one the people were to choose between (A) a sure gain of $240 or (B) a 25 percent chance to gain $1,000 and a 75 percent chance to gain nothing. In row two they were to choose between (C) a sure loss of $750 or (D) a 75 percent chance to lose $1,000 and a 25 percent chance to lose nothing. Two simultaneous decisions; what did they choose?

On the surface that might seem odd. After all, if you’re willing to accept the possibility of a larger loss, why wouldn’t you also “roll the dice” for the chance at a larger gain? However, the majority of us don’t look at this as a pairing of decisions, but as two separate decisions.

The first decision relates to gains, and most of us tend to be risk-averse when we have a profit. An example is that people often sell their winning stocks because “you’ll never go broke taking a profit.”

The second decision relates to loss, and we tend to be risk-seeking when we have a loss. Not only do we hold on to our dodgy stocks, but we might even “dollar-cost-average” and buy more shares.

Buying life insurance works against our normal instincts. Say you could buy a life insurance policy with a $240 premium and a $10,000 death benefit. The pair of decisions now in row one are choose between (A) a sure gain of $240 (money we save if we don’t buy the life insurance) or (B) a perceived 1 percent chance to get $10,000 and a 99 percent chance to gain nothing.

In row two choose between (C) a loss of $240 spent on the premium if you don’t die or (D) a 1 percent chance to lose $10,000 and a 99 percent chance to lose nothing. Our head is telling us to not spend the $240 premium (A) because (D) there’s a 99 percent chance the bad thing won’t happen. However, an insurance purchase requires us to choose (B) and (C).

This is a huge point. The life insurance frame is contrary to the way most people think because it requires them to seek out risk and pay an expense when everything seems like it is going well and they don’t feel the risk.

This narrow framing goes far in explaining why life insurance is a hard sell—because it is 180 degrees from the way seven out of eight of us would typically decide.

How do you fix this problem? You need to change the framing.

Change The Odds Framing

People who are told that they have a terminal illness often become motivated life insurance buyers. A $240 premium on a $10,000 death benefit seems cheap if there’s a 99 percent chance of dying; yet not so cheap when there’s a 99 percent chance of living.

This frame can be changed if the perception of the likelihood of death is changed even if the real odds haven’t. A person’s perceived likelihood of dying increases if his best friend dies from a heart attack. Although I once had a colleague who handed out business cards at funerals, another way to alter the perception of death occurring might be to share obituary notices of people dying at the same age as the prospect. Telling prospects stories of similar people dropping dead can help to reset the frame.

A better way, which will cause fewer compliance issues, is to change the cost framing: Would your family rather have 50 cups of coffee or $10,000?

Change The Cost Framing

Most will agree that $240 is a meaningful sum of money, and most can think of several things to do with that amount of money—but unfortunately (for us) paying for life insurance isn’t high on their list. However, $240 is roughly the cost of buying a large cafe latte each week at a local coffee house. Instead of talking about spending $240, ask your prospect whether he would rather leave his dependents 50 cups of coffee or $10,000, because the cost is the same for both.

There’s a reason you still hear ads saying that for “pennies a day” you can have this or that—it works. Breaking a cost into smaller pieces makes the overall cost seem smaller. A possible $10,000 payoff on $240 isn’t bad, but a $10,000 payoff on $20 (the monthly cost) sounds even better.

The Underwriting Coupon

A significant number of consumers who don’t own individual life insurance policies say one of the reasons is that the buying process is too difficult. For buyers accustomed to instantaneous transactions on the internet, the life insurance process may seem tedious.

A way to deal with this concern is to frame this extra hassle as a method to get a discount from full price: “Yes, I can offer you life insurance that doesn’t require a physical or phone interview, but it costs $800 a year. However, if you agree to volunteer some personal information, I might be able to get you a sizeable discount off that regular price.”

Your prospects probably have club cards or memberships with a number of retailers, all of whom promise to give them a better deal if they agree to share personal information or buying habits. Basically, what you’re doing is presenting the extra hassle as a way to get the “coupon” that reduces the retail price. Not paying full price by redeeming the “underwriting coupon” makes them feel like smart shoppers.

Present the hassle of underwriting as receiving a smart shopper discount for sharing personal information.

The old solution is to find guaranteed issue policies where underwriting can be avoided altogether or find very lenient insurers, but this often results in overpaying for insurance or the customer getting less coverage than needed. However, if you frame the underwriting process as something smart shoppers do, explain what underwriters look for, what is and is not a deal breaker, and how putting up with the hassle may allow the buyer to save money, you will get more people to submit an application.

Framing won’t help you close every sale, but these concepts will help you close more sales by enabling you to deal with the mental biases that often distort the decision-making process. I’ll talk more about life insurance and framing down the road.

"Let me think about it” is often a prospect’s polite way of saying that he doesn’t want to buy what’s being offered. However, that response can also mean that the agent wasn’t able to penetrate the decision-making biases that result in no decision being made.

Following are a few of the biases and what can be done to overcome them.

The endowment effect is a bias in which clients place a higher value on an item simply because they own it. A good example of this is trying to get more than market value because of the sentimental value that is attached to the item.

An obvious solution to the endowment effect is to show the seller what the true market value is; however, this doesn’t work when a seller refuses to recognize that his widget is the same as another widget.

The good news is the endowment effect doesn’t apply to money or money equivalents. A seller may think his collectible pin is worth $6 even though the same pin can be bought for $5 elsewhere, but he doesn’t think his $5 bill is worth $6.

Along the same lines, people don’t think their mutual fund, annuity or certificates of deposit are worth more than market value simply because it is theirs, so the endowment effect usually does not get in the way when you are trying to convince a client to move those dollars to a new annuity or insurance policy. However, there are other biases that do.

Anchoring is the mental value we give an item, based on a past value rather than current value.

Here’s how anchoring can interfere: A potential fixed annuity buyer has witnessed his IRA drop in value from $100,000 to $75,000; he is tired of losses and likes the protection a fixed annuity offers. However, he won’t transfer his IRA to the annuity because he can’t afford the $25,000 loss.

The reality of this situation is that he has already lost because the IRA is worth only $75,000, but in his mind the value is still $100,000.

One way to deal with the problem caused by the old value is to provide a new value—acknowledge the old anchor by referring to it as a loss.

“Your IRA has lost $25,000 and is now worth $75,000.” You then raise the possibility of future losses: “While we don’t know what will happen in the future, how would you feel if your IRA were worth $50,000 next year because you didn’t transfer to the annuity today?”

What we’ve done is replaced the $100,000 anchor with a new anchor of $50,000. Thus, preserving the $75,000 by transferring the IRA to the annuity is more attractive because it avoids losses.

Another way to move the anchor is to refer back to the lower original purchase price. If the person had initially placed $50,000 in the IRA and the current value is $75,000, talk about doing the transfer to preserve the remaining $25,000 gain.

In the previous example we suggested the person avoid future losses and in this example we suggest preserving past gains—both are ways to move the anchor to reflect reality and help your client make a good decision.

Sunk cost bias is about costs incurred that cannot be recovered and is familiar to anyone who has ever owned an over-the-hill car. The story goes something like this: The clutch in your 87 Yugo went out and will cost $600 to fix. If you had any sense you would junk the car, but just last month you spent $250 to replace the water pump and there was the $500 you spent for tires last winter. You have a lot of money in this car and you hate to lose it by getting rid of it. The reality is that the money spent on the car is already lost—and you can’t get it back. Thus, what you have spent in the past is irrelevant, but it doesn’t feel irrelevant, so you want to keep putting money into a losing proposition.

Sunk cost bias is a reason bad projects continue in the business world (Microsoft Zune, anyone?) and a main reason that people hold on to bad investments.

The solution is simple: Ask your client, “Would you buy that same bad investment today at its current price?” If his answer is no, then move on to something new.

Then there is the age-old problem of inertia. The 18th century essayist Samuel Johnson said, “To do nothing is within the power of all men,” and not making a decision is a way to do nothing. There are several ways to deal with inertia.

• Deadlines. If the rate is really going down tomorrow, there is definitely motivation to do something today—but even if it isn’t, giving your client a brief history lesson may help.

“Do you remember two years ago when I said you could lock in a 5 percent annuity rate for five years and you said you wanted to sleep on it? Do you remember last year when I could get you 3.5 percent for five years and you said you wanted to sleep on it? Today, I can still get you 2.5 percent for five years…or do you want me to come back next year when the rate is 1.5 percent?”

• No Decision Is a Decision. The problem with making a decision is it may be a bad one and risk aversion means people avoid the risk by doing nothing. When a client says he wants to delay a decision, it sometimes helps by framing this delay as a decision.

“So you’ve decided now is not the time to protect your family from financial ruin by signing this life insurance application?”

• Overpowering inertia. Most people won’t bend over to pick up a penny, but almost all would quickstep across a road if they saw a $20 bill in the gutter. If the reason for making a decision is compelling, it overpowers the inertia. This can be done by making the benefits of your solution more vivid.

“If rates stay the same, this index annuity could earn you more interest by next year than your CD could earn by 2021.”

Or by using loss aversion: “How would your children eat if you get hit by a truck tomorrow?” Or bringing the future into the present may be the ticket: “If you act now you can be guaranteed your $100,000 premium will produce a lifetime income of $10,000 each year in retirement.”

The two self-inflicted mind games driving all forms of decision-making inertia are a psychological need for consistency (we don’t like change) and the cognitive biases that interfere with rational reasoning.

Now you have some ammunition to help you deal with decision-making biases. These suggestions aren’t a cure-all, because biases are difficult to overcome, but they will help you help your clients make better decisions.

Many studies have found that a non-mentally impaired 75- or 85-year-old can make financial decisions that are just as good as a non-mentally impaired 25- or 35-year-old. Indeed, a 75-year-old often makes better decisions than a 25-year-old because if an older person has had to face a similar decision in the past, he uses that experience to guide him. However, an older person processes data in a different way than a  young one, and this must be taken into consideration. In addition, the odds of being mentally impaired dramatically ramp up once a person reaches the eighties.

Crystallized Versus Fluid Intelligence

To understand how aging affects the brain, think about how a computer works—it takes in data and then processes that data to reach a conclusion. The more data that is already stored on the hard drive, the less new information is needed. In the brain this stored data is referred to as crystallized intelligence. A 75-year-old typically has much more stored data than a 25-year-old, thus making a fresh decision doesn’t need to wait for all of the data to come in from the outside.

The computer also needs to process data using it central processing unit (CPU), but not all computers process data at the same speed. A Xeon or Core processor works much faster than the Intel 8086 processor that premiered in 1976. In the brain the CPU is known as fluid intelligence and the reality is that our fluid intelligence (processing speed) peaks around age 20 and steadily drops over our lifetime. However, because our crystallized intelligence grows at a faster rate than our fluid intelligence declines, we are able to make better decisions as we age.

There does come a point when our stored data cannot offset the loss of processing speed and the quality of our decisions gets worse. That doesn’t mean these decisions are necessarily bad, it simply means they aren’t as optimal as they once were. There are steps that can be taken to optimize senior decision-making.

Improving Senior Decisions

Give Them Time. If seniors are not pressured and not rushed, they tend to make decisions as well as anyone else. This is by far the most important element in making better decisions. Take breaks and don’t hurry.

Be Repetitious. Since there is so much data already stored on the brain’s hard drive, it may take repeated exposure to get new data to sink in. On key points, probe for understanding by asking what that key point means to them.

No Multitasking. Regardless of age, doing two or more tasks at the same time results in a worse performance than doing each task separately. However, the consequences of multitasking are much worse for seniors. What this means is a presentation should be done in an atmosphere free of distractions (no television or other background noise). Concepts and benefits need to be presented one at a time so that you have a client’s complete attention.

Give Enough Choices, But Not Too Many. When seniors were asked how many options they wanted to choose from, the average response was four choices when it came to picking a Medicare plan, five choices for a doctor or jar of jam, and six choices for an apartment or car. This does not mean a senior should be denied the availability of selecting from among 8,000 mutual funds or 600 annuities, it means that a financial professional needs to act as a data-sorter in order to make decisions manageable.

Connect Emotionally. If a presentation consists mainly of spreadsheets, charts and other cold facts, decision-making becomes more difficult. Seniors respond better and remember more information when it is emotionally charged. So, instead of saying this life policy provides a $10,000 death benefit, a senior will do a better job of processing if you mention that John and Mary bought a similar policy and, when John passed away, the life insurance paid for the funeral and Mary didn’t have to worry about covering the cost.

Dementia and Impairment

A non-mentally impaired senior can make financial decisions that are just as good as a non-mentally impaired “junior,” especially when data is presented in a manner that optimizes mental powers. The problem is that a senior is more likely to be impaired than a younger person.

The terms dementia, Alzheimer’s and mental or cognitive impairment are often thrown about as if they mean the same thing—but they don’t. Dementia, which includes Alzheimer’s, refers to the deterioration of mental faculties to a state in which fully rational decisions cannot be made. Cognitive impairment means the brain may still be able to make quality decisions, but there are impairments that may at times interfere with the process. A person with dementia is cognitively impaired, but a cognitively impaired person may not have dementia.

Under age 70, the odds of the client across from you having dementia or being cognitively impaired are relatively low. Even looking at clients in their seventies, the odds of their having any form of cognitive impairment are less than one in five. However, if you look at people in their eighties, the odds of impairment increase to one in two, and those with full-blown dementia increase to one in four.

During the course of an appointment it can be impossible to tell whether the person across from you might simply be having a “senior moment” (isolated forgetfulness) or is cognitively impaired. There are a variety of exams designed to test specifically for impairment, but that raises questions of the role and responsibility of an agent.

What if an agent sells a policy and, subsequently, it is discovered that the buyer was cognitively impaired? Every insurance carrier I spoke with said if it were brought to their attention that a buyer was impaired at the time of a purchase, the policy would be canceled and the premium refunded, if desired.

What happens if someone alleges that an agent knew a buyer was cognitively impaired and used the impairment to “financially exploit” that buyer by selling him a policy? In 37 states that buyer could sue that agent for civil damages, but it is not a crime.

However, 13 states have criminal sanctions against the financial exploitation of vulnerable adults (Alabama, California, Connecticut, Delaware, Georgia, Idaho, Kentucky, Louisiana, Mississippi, Nevada, South Carolina, South Dakota and Tennessee). In these states a financial professional could conceivably be convicted and sent to prison for financial exploitation if it is determined that a consumer was impaired at the time of a sale—even if no fraud had occurred (this list of states is accurate at this writing, but it is not warranted and should not be taken as legal advice).

Summary

Agents need to recognize that seniors’ decision-making is different from juniors’ and should take this into consideration regarding how to interact. Agents must also recognize that a 75-year-old can make a decision just as well as a 35-year-old if the presentation takes into account these differences.

The risk of a consumer being cognitively impaired dramatically increases once the eighties are reached. The two truths are that consumers in their eighties need financial products and that a significant percentage of those have impairment. Agents who wish to work with this market will need to establish their own guidelines and procedures on how to deal with this issue.

If you do sell a policy to a person with dementia and some regulator says you “financially exploited” that person, the odds are very, very slim that you’re going to be sentenced to prison (although it has happened—once). In 37 states this isn’t a criminal offense. In the remaining states the definition usually means actual theft of assets, and purchasing an insurance product does not appear to meet that definition.

If you walk into the grocery store tomorrow and the cheapest can of coffee is $40 for 12 ounces, you will likely walk out without buying that coffee. However, you might readily pay $4 for a café latte at your local coffeehouse—and that works out to a lot more than $40 when all is said and done.

Closer to home, if you put a sign in your office window tomorrow that says: “Lottery—$100,000 prize, $1000 per ticket but you are guaranteed to win $100,000 prize as long as you keep playing,” you would have people lined up around the block. And yet those same people who would buy that ticket (because the prize would take care of their families) won’t give you a $1,000 premium for a $100,000 whole life policy that is guaranteed to one day pay off as long as they keep playing.

The way the data we receive is framed affects our decision. The right words can remove potential objections and motivate action. For example, to show how safe fixed annuities are, many agents start by bashing the safety of banks. However, my research indicates that it is better to simply say to the consumer: “Fixed annuities are safe; do you know anyone who has ever lost money with a fixed annuity?” The response from almost everyone is “No.” What you have just done is helped them decide for themselves that fixed annuities are safe.

Another example is to ask a prospect, “What are you earning on your certificates of deposit?” rather than first announcing the current annuity yield. By doing this, your annuity’s yield will sound much better after a prospect is reminded what his bank is paying.

The right words can motivate people to act. You are more likely to prompt an action by saying, “Are you going to take a walk after dinner?” rather than “Any plans for the evening?” The question: “Have you guaranteed that your essential retirement expenses are covered?” can be more pithily stated by, “Have you made sure you won’t wind up out on the street?”—and is more likely to get someone to buy an annuity than asking them, “Are you interested in annuities?”

The right words can help people identify an unrealized need. The feature of lifetime income from an annuity may not seem like much of a benefit if a prospect thinks he is going to die next Thursday. Instead of beginning the presentation by proclaiming the wonders of a lifetime income, instead ask, “Did you have any relatives who lived into their nineties?” By introducing the longevity risk this way, the prospect begins to recall every relative who lived a long life. Thus, when you say “This annuity means your income will last as long as you do,” the benefit becomes apparent and real.

Another example of framing is to introduce cognitive dissonance into the equation. You may remember from psychology 101 that this means holding a belief that is contrary to our actions. When our actions and beliefs conflict, a level of discomfort—or dissonance—is created; and the way we typically relieve the discomfort is by changing our actions (which is usually easier than changing our minds).

In the context of an annuity, you would ask the couple across the table, “Would you agree that it is better to earn a little less rather than risk losing your money?” and “Would you agree that it is important to protect at least some assets from the risk of stock market loss?” You would then ask, “Are some of your retirement assets protected from the effects of a prolonged bear market?”

If the couple has said that it is important to protect assets and then is confronted with the self-realization that they have not, then dissonance has been created. This stress can be released by saying, “Were you aware that a fixed annuity can protect your assets from the risk of stock market loss?”

Framing by creating and relieving dissonance is helpful in many areas. Asking a prospect, “Is it important to you to make sure your children won’t have to be responsible for your funeral expenses?” followed by asking whether his funeral expenses are covered may show a need for final expense insurance.

Another example of framing is asking a 35-year-old client whether it is important that his children are able to go to college and exactly how that will be accomplished if he is dead.

A key point is that framing does not make someone do something they shouldn’t do—it isn’t a magic spell. What framing can do is help clients examine data in a new way so that they can make an informed decision.

We like to think that clients make rational decisions. That they take in all of the data we are presenting, process this data, and then they reach a conclusion that maximizes the utility of their economic behavior. However, that doesn’t always happen.

Often, emotions come into play and affect our decisions. Indeed, there are people who say that decisions are completely emotional, but that isn’t true. The motivation behind a decision based purely on emotions fades away when the emotion fades away, and often the result is a canceled sale.

The biggest problem agents have is not in dealing with emotions or ensuring that clients have adequate data, it is penetrating the fog that keeps the agent’s story from being heard at all. Before the data can even get to the client’s brain to be processed it needs to penetrate a fog of mental biases that can distort the agent’s message or completely block it.

There are a couple dozen cognitive biases out there that can create a fog, resulting in less than optimal economic behavior. What I hope to do is help you clear away the fog so that your clients have the data needed to make decisions. What I’ll typically do is describe the bias and then offer a solution or two. Does the solution always work for everyone? No, with some people it can be close to impossible to eliminate a bias, but in most cases the concepts will help you get your message across.

Projection Bias is taking what happened yesterday and today and saying it will happen tomorrow. Sometimes it is right—August usually is as hot as July. Sometimes it is wrong—as in predicting the Dow at 36,000. Sometimes the bias helps a client listen to your story; e.g, a client notices that bank rates have been dropping, which is why she is now interested in the annuity idea. But often this bias gets in the way.

Suppose you have a client who is temperamentally unsuited for the stock market because such people always buy at market tops and sell at market bottoms (a perfect illustration of the problem caused by projection bias). Since the market has been heading up, as of late, the client once again wants to invest in the market. You can help the client by showing him a chart of how the stock market has performed since the century began, rather than over the last few months. Seeing the bigger picture reminds the client that he didn’t do well on this ride and may fare better on one that isn’t as volatile. Changing projection bias often simply means changing the dates on the chart. A stock market chart starting in 2008 conveys a different impression than one that starts in 2001.

Vividness Bias means the more vivid the data seems the greater our reaction, which is why the evening news doesn’t lead with the chess tournament scores. To get your story across means you need to make your strengths as vivid as possible. An example would be if your annuity has a 2 percent rate and the competitor offers a 1 percent rate, you would say you offer 100 percent more interest.

Another example would be if a client is trying to decide between a 0.3 percent bank certificate of deposit or the fixed index annuity that could earn up to 3 percent if the index cooperates. One way to explain this is to show that if rates don’t go up and even if the index annuity hits only the 3 percent mark this year and never again, the CD account won’t catch up with the index annuity until 2022. The story is, “Would you rather have your potential interest earned in 2013, or do you want to wait until 2022?”

Choice Conflict means that the client is presented with so many choices that he fails to make a decision in fear of making a bad one. That doesn’t mean telling the clients that they can’t choose from every variety in the candy store, it means helping them narrow down their choices. You can explain that they can have anything they want, but then mention that other clients with similar needs have chosen either Plan C or Plan R. Even complicated decisions can be handled by breaking them down into smaller parts with fewer choices. If at all possible, try to get the choices down to two.

You can help your client make better decisions if you eliminate or work around the cognitive biases that get in the way. My mission is to make you aware of what these are and how to deal with them so that you close more sales. 

The 10-year U.S. Treasury note closed out 2012 yielding 1.76 percent. However, based on history it should have been 3.25 percent because the average Aaa corporate bond yield was 3.62 percent. Therefore, U.S. Treasury yields will increase in 2013 even if overall rates do not go up, because they are abnormally low. Here’s the reasoning.

If you look back over the last half of the 20th century you find that the yield on the 10-year U.S. Treasury note averaged 90.5 percent of the yield of Aaa corporate bonds. What this means is when the Aaa corporate bonds were yielding 5.0 percent the 10-year Treasury was at 4.5 percent; if Aaa corporates were at 10 percent, the 10-year Treasury was at 9 percent. In the early 1950s when the yield on Aaa corporate bonds was at 2.8 to 3.0 percent (even lower yields than today), the yield on 10-year U.S. Treasury notes was 2.5 to 2.7 percent.

Relative to Aaa corporate bond values, Treasury yields tracked very closely through both good times and bad. Until the Asian credit crisis in 1998, 10-year Treasury yields were never less than 79 percent of Aaa corporate yields, and even that ratio was quickly back over 80 percent by 1999.

The early 2000s were a different story. The millennium recession, although mild in the United States, caused credit problems in foreign markets, and that fear fueled an unjustified fear about U.S. corporate debt. Between the summers of 2002 and 2003, Aaa corporate bond yields averaged 6.1 percent, which should have meant a 10-year Treasury yield of 5.5 percent, based on history. Instead, however, Treasury notes paid 4.0 percent, or 66 percent of the Aaa bond yield, rather than 90 percent. Based on fear of foreign contagion and a possible double dip recession, investors did not so much as build in a risk premium for owning corporate bonds; instead, they created a safety rate discount for owning government debt. This caution proved unwarranted and those who had purchased Treasury notes instead of Aaa corporate bonds experienced relative losses as the correlation between the two steadily increased to 88 percent by 2007. To put a head on it, during this period, 10-year Treasury yields rose from 3.9 to 5.1 percent (and the market value of issued T-notes declined) and Aaa corporate bond yields barely moved from 5.7 to 5.8 percent (thus, market values barely stuttered).

The financial crisis of 2008 shook investor confidence and the credit markets to the core. Once again there was a flight to treasuries, but the safety discount became even more drastic with 10-year treasuries yielding less than 50 percent of Aaa corporate debt yields by the fall of 2008. This ratio steadily improved in 2009 and reached 73 percent by the spring of 2010, before slowly falling back again as worries increased about the strength of the recovery. However, the next stumble was strictly due to the summer 2011 folly in Washington with the debt ceiling.

In August of 2011, due to concerns that the United States might default on their bonds, the ratio again dipped below 50 percent and then got worse. By the time of the 2012 election the yield on 10-year treasuries was half of its long term ratio—45 percent. To put this into historical perspective, the 10-year U.S. Treasury note should have yielded 3.25 percent in November 2012—instead it was at 1.59 percent.

Simply put, based on the actual risk of default on corporate debt, the 10-year Treasury yield is far too low. Even if we approach this from the other side and say corporate yields are too high and should come down to the 2.7 percent range (only briefly seen 60 years ago), it would still mean 10-year Treasury notes should be yielding 2.5 percent today.

Different Standards for Today?

Because today is a different world than that of the last half of the 20th century, there is the possibility that the old metric of 90 percent may be obsolete because Aaa corporate debt is much riskier than U.S. debt—although the evidence doesn’t support this contention. However, even if the new ratio is 66 percent (which was the bottom ratio briefly a decade ago), this means 10-year treasuries should yield 2.4 percent today. If the new ratio is a much more likely 80 percent, it means 10-year Treasury notes should gradually increase to yield 2.9 percent even if overall interest rates do not increase.

Once again it appears the financial markets have severely overreacted to the possibility of default of investment grade corporate debt. However, rather than increasing the yield on the corporate debt, the market’s reaction has been to give a safety rate discount for U.S. debt, which is unwarranted. The end result is that even if rates on corporate bonds do not improve, the yields on Treasury debt will go up over the next couple of years, bringing a measure of relief to insurance carrier investment portfolios and allowing them to raise rates.

Neither Jack Marrion nor Advantage Compendium sell or endorse any financial product.