When most financial professionals measure risk, they typically have conversations about Standard Deviation, Sharpe Ratios, and Monte Carlo simulations.  These risk-measuring techniques are certainly important topics of discussion because they address the Probability Of Risk, however they all fail to articulate the most important element of risk: The Consequence Of Risk.  

I was once asked if my proprietary backtesting software ran Monte Carlo simulations.  I understood the spirit of their question, however I explained that Monte Carlo simulations merely measure the Probability Of Risk.  So I asked him what level of risk he was comfortable with.  80% probability of a positive outcome?  90% probability of a positive outcome?  

The gentleman told me he was comfortable with a 90% probability of a positive outcome (10% probability of a negative outcome).

I said, “Okay, if you’re flying in an airplane and you have ten parachutes to choose from… and nine of them work, you have a 90% probability that things will work out just fine.  Are you willing to blindly pick one and jump out of the plane?”

His comfort level with 90% probability of a positive outcome quickly faded.  In this scenario, the Probability Of Risk was relatively low, however the Consequence Of Risk was extremely high.

In premium financing, I focus on modeling what happens in a scenario where a severely negative set of circumstances occurs, even if the probability is only 1%.  Though I am concerned about the Probability Of Risk, I am far more concerned with The Consequence Of Risk.

The Consequence Of Risk in premium financing is severe – a lapsing policy – which can happen if the compounding debt of interest accrual outpaces the policy value’s growth.  However the risk of interest accrual is not solely limited to the third-party loan debt balance.  Its liability also exists in the debt accrual that occurs in a Participating Loan with the carrier.  If the cash value growth (due to the index credit) does not outpace the policy charges and the internal accrued interest debt, the net value of the policy may erode, potentially resulting in an early policy lapse.  The probability of this happening drastically increases when a premium financing arrangement is too aggressively designed wherein the client does not have enough skin-in-the-game.

The Probability Of Risk in an over-leveraged, overly-aggressive premium financing arrangement is too high for most clients’ financial strength and risk tolerance levels.  I provide the premium financing seatbelt (reducing the Probability Of Risk), as well as the simulated crash test results (modeling the Consequence Of Risk) in order to evaluate whether or not premium financing is appropriate for the client.

Sure, there are extremely rare exceptions where high-risk tolerant clients choose to employ hyper-aggressive leverage-on-leverage tactics, but for the most part, life insurance should be a conservative piece of a client’s overall financial portfolio, especially when it comes to estate tax planning.  I specialize in the conservative approach to premium financing.

Though I am not a CPA or tax attorney (which means that I cannot give tax advice or legal advice), I can mathematically model different premium financing strategies, compare them to non-insurance-based solutions, and showcase different financial outcomes using certain tax assumptions.  I can also model certain unfavorable assumptions that put extra strains on the premium financed solutions, which I think is vitally important to model for clients in the spirit of full transparency and proper due diligence.  

This industry lacks full client disclosure, so being the beacon of transparency – albeit a self-proclaimed title – is the main reason I am the trusted source of premium financing for top advisors, producer groups, CPAs, tax attorneys, family offices, and carriers in the life insurance industry.

If the spirit of advisor/client conversations is rooted in education, consumer protection, and risk mitigation, then the foundation of these conversations will be client-centric, which is what they should be.  My goal in all communication efforts is to transparently articulate how the math works, and if the indisputable math tells us that one particular method of premium financing is the most advantageous to the client compared to other viable alternatives in an array of backtested scenarios (I analyze 121 different historical 40-year periods), the decision to move forward with that particular design is an obvious one.  I have devoted an entire chapter to explaining how my backtesting software works.

Conversely, there have even been scenarios where my mathematical modeling process proved that it was not in the client’s best interest to finance their life insurance premiums, and in such scenarios, I am the first one to discourage them from using premium financing as a strategy.  Some advisors don’t like that I’m so blunt with my recommendations against premium financing in these scenarios, and some of them will even try to get me to rework the numbers to favor the premium financing proposition.  In these rare instances, my answer is always the same: The math either works, or it doesn’t work.  The math doesn’t lie.

Remember, I’m the guy that won’t even sugarcoat a cookie.

Your journey in understanding the true Consequence Of Risk in poorly designed Premium Financed IUL policies starts with understanding how policy charges and credits actually work.  

When a carrier receives the policy premium for an IUL product, the first charge that is deducted is the Premium Load.  Assuming the premium is paid annually at the Beginning Of The Year (BOY), the Premium Loads are also deducted at the BOY.  The remaining policy charges (e.g., Cost of Insurance, Administration Fees, Mortality Expenses, etc.) are then deducted monthly.

Assuming the client selected a 1-Year Annual Point-To-Point index option, the segment begins in the month the premium is swept into the index account (e.g., the 15th day of the month), and ends twelve months later.  At the end of this 12-month segment, the index credit is then determined based on the underlying index’s performance during that 1-year segment wherein the IUL’s cap and floor would be applied to the gross accumulated value of the policy (not the net cash surrender value, but the gross accumulated value).  Some carriers apply this index credit to the EOY Accumulated Value (EOYAV), whereas other carriers apply the index return to the Average Monthly Accumulated Value (AMAV).  

Mathematically speaking, the AMAV is a higher number than the EOYAV because it does not account for 100% of the monthly charges.  In other words, in the second month of the segment, only 2/12 of the monthly charges have been deducted, hence the accumulated value in that month would be higher than the accumulated value in the eleventh month wherein 11/12 of the monthly charges were deducted.

For the sake of this discussion, we will assume that the Indexed Universal Life (IUL) insurance product applies the index credit to the EOY Accumulated Value.  We will also assume that the product’s underlying index tracks the S&P 500’s performance, with a 0.00% floor and a 9.00% cap.  In the event that the S&P 500 produced a positive return of 15.00% in a given year, the policy index credit would credit 9.00%, not exceeding the maximum allowable return (the cap).  

   $1,000,000  Previous Year’s EOY Accumulated Value

+               $0  New Policy Premium________________ _

   $1,000,000  Current Year’s BOY Accumulated Value

-       $50,000  Current Year’s Policy Charges                                                       .

      $950,000  Current Year’s EOY Accumulated Value (Before Index Credit)

x         9.00%  Index Credit (assuming a +15.00% S&P 500 Return & 9.00% cap)

        $85,000  Accumulated Index Credit (Accumulated Value Gain)

+    $950,000  Current Year’s EOY Accumulated Value (Before Index Credit)

   $1,035,500  Current Year’s EOY Accumulated Value (After Index Credit)

However if the S&P 500 produced a negative return in a given year, the index credit would be 0.00% (the floor).  This stop-loss feature of this particular crediting method acts as a risk-mitigation tool, which is certainly one of the most valuable elements of the IUL product chassis.

However, one of the most inaccurate statements I’ve heard some life insurance agents say is, “With the IUL’s 0.00% floor, you can never lose money.”  Mathematically speaking, this is not a true statement.  It is true that you would not receive a negative index return (an index return less than the 0.00% floor), however that is only true AFTER the policy charges have been deducted from the policy value.  If the policy charges were $50,000 in a given year, and the BOY cash surrender value was $1,000,000 (assuming a 0.00% index credit in such year), the EOY cash surrender value would be $950,000.

   $1,000,000  Previous Year’s EOY Accumulated Value

+               $0  New Policy Premium_________________.

   $1,000,000  Current Year’s BOY Accumulated Value

-       $50,000  Current Year’s Policy Charges                                                       .

      $950,000  Current Year’s EOY Accumulated Value (Before Index Credit)

x         0.00%  Index Credit (assuming a -15.00% S&P 500 Return & 0.00% floor)

                 $0  Accumulated Index Credit (Accumulated Value Gain)

+    $950,000  Current Year’s EOY Accumulated Value (Before Index Credit)

      $950,000  Current Year’s EOY Accumulated Value (After Index Credit)

In this example, the policy’s Accumulated Value would have actually decreased by $50,000 despite the 0.00% floor.  Aside from premium financing, the first risk factor to understand in an IUL is the relationship between policy index credits and policy charges.  

One of the problems I have with clients making buying decisions solely based on standard carrier illustrations is that they depict a positive static index return every year with no simulations of volatility wherein 0.00% index returns are modeled (as they were in the example I just explained).  

What this means is that the discussion (and mathematical modeling) of negative arbitrage during 0.00% return years is never properly articulated (and certainly never mathematically stress-tested) in most advisor/client discussions.  

One of the main things that makes me stand apart from other premium financing intermediaries is my ability to mathematically model scenarios wherein these design elements (e.g., floors, caps, charges, etc.) can be modeled during times of volatility so you can actually see the potential effects of these different variables.  

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