Exceptional Longevity Predictable

A genome-wide association study (Paola Sebastiani et al) based upon 1055 centenarians, showed that Exceptional Longevity (EL)  - living 90 years or more - can be predicted with 77% accuracy!

video

EL Genetic Passport
This research development will have major impact on 'life insurance' and pensions. With an EL Genetic Passport in your pocket, you'll have the power to conclude with 77% certainty whether it's profitable (or not) to buy life insurance or to invest more or less in your pension fund.

Genetic Loss by GAS

To prevent major losses caused by 'adverse selection', life insurance companies and pension funds have no other choice left, than to base life insurance premium prices and pension contributions on 'genetic passport information'.

Just like it's (from a company's perspective) devastating to sell mortgages to people who cannot afford it, it's also killing to sell life annuities to people who have knowledge of getting 90 years or older with 77% certainty.

As Genetic Adverse Selection (GAS) also negatively affects current provisions and value of an insurance company or pension fund, GAS development effects should be included and estimated in actual liability calculations.

Without doubt, GAS will generate large Genetic Losses in the next decades. Perhaps GAS can be qualified as a substantial new kind of risk in Pillar I calculations.


Related links - Sources:
- Science: Genetic Signatures of Exceptional Longevity in Humans
- PDF: Genetic Signatures of Exceptional Longevity in Humans
- BU: Signatures of Human Exceptional Longevity (video)
- Centenarians in some European countries, 2007

Death by Solvency

Risk Management can be a strange and deathly game. Normally one would expect that the more the demand of Probability of Insolvency (POI) is decreased:

• the more Prevention- , Risk-reduction- and Damage-control-measures will be taken
• the less actual Risk and corresponding Loss will actually occur
• the higher the resulting average yearly profit
• the lower the resulting yearly profit volatility

This appears to be true in situations where Risk Management is hardly developed and POI-Demands are relatively modest (5%-2.5%).

Increasing POI-Demands
However, depending on the type of risk, beyond certain POI-Demands (smaller than roughly 2.5%) , the costs of Risk management measures, maintenance and capital requirements become higher than the average expected Loss-reduction, resulting in - on average - lower profits.
Of course, these extra risk management investments and capital requirements can financed by raising consumers prices, but - on balance - this will result a smaller market corresponding with a lower profitability level.

The question can be asked if this still is what we, management and consumers, intended to achieve.......?

Next, in our passion to reduce Risk to an even more extreme low level, we can get carried away completely...

Excessive POI-Demands
When POI-Demands get to levels of 1% or less, a remarkable psychological effect enters the Risk management arena.

Management perceives that the Risk-level is now actually so low that they cannot fail anymore.
In their ambitious goal to outperform the profit level of their competitors, management gets overconfident and reckless. What would you attempt to do if you knew you could not fail?

When POI-Demands are set to levels of 0.5% or less (as they are mostly now in 2010) it becomes almost impossible to beat your competitors with an approach of 'taking more risk'. Even if one would try to manage or hedge these extra risks 'best' in the market. In the long-term, the price of this risk would equal or beat the expected loss.

In this situation some managers get desperate and instead of considering things 'right', they see only one option 'left'....

WAR

'Working Around (the) Rules"

WAR, Working Around the Rules, comprises actions like:

• Taking (extra) risks on non-measurable or non-measured financial transactions, or or 'non-obligated-reporting risks'
• Manipulating, disguising or mitigate risk information, or risk-control reports
• Misuse legally allowed methods and accounting principles to create legally unintended financial effects or transactions

 

It's perhaps hard to admit, but as actual developments show, we've entered the final WAR phase. Some Examples: subprime, Madoff accounting, BP-deep horizon oil failure, bank multipliers, etc, etc.

In all these examples, managers (are pushed to) become over-creative by working around the rules to deliver what they've promised: more profit.

However this approach always results in

1. More short-term profits
2. Less long-term profits
3. Sudden bankruptcy in the end

This development, resembles the 2010 situation in the Insurance and Banking industry where, after each financial debacle, POI-Demands where successively decreased to a 0.5% level  and have resulted in marginal profits and a highly volatile Profits or even losses. Pension Funds (NL: 2.5% POI-Demand) appear to be the next patient the operating table.....

The situation is out of control. Nothing really seems to help anymore....

Solutions

Are there any solution to prevent this solvency meltdown process?
Yes, but that's for another blog as this one is getting too long...

Related links:
- Why excessive capital requirements harm consumers, insurers and...(2010)
- Presentation - Modelling of Long-Term Risk (2010)

Risk Symptoms Matrix

On INARM (International Network of Actuarial Risk Managers) ERM advisor Dave Ingram raises the simple question:

What must managers who are not modelers know about models?

Perhaps this question is one of the most relevant questions in Risk Management and the Actuarial profession. It's a key question that should be discussed on Board Level in every (financial) area.

Also this question is relevant in setting up and managing complex projects like Solvency II, ERM, Pension Fund Risk Management, ALM and even "In control" projects.

The answer
Now let's try to answer this intriguing question

Managers are experts in 'decision taking'. Modelers are experts in reducing and simplifying complexity to decidable parameters.

Now the Quality (Q) of a management decision (D) is defined by the equation:

[ Q(D)= Q(Manager) x Q(Modeler) ],

where Modelers are responsible for the Quality of the Input (data) of the model [Q(Input Model)] and the Quality of the modeling process itself [Q(Modeling)].

More refined, we may therefore define :

Q(D)= Q(Manager) x Q(Input Model) x Q(Modeling)

Luckily, not all Q's are independent!
Both Managers and Modelers can raise the Quality of the outcome of the Decision process by asking each other "What If" questions.

By asking WI-questions with regard to the 'Input of the Model" [Q(Input) = data, decision parameters] and examining the output, Modelers are able to raise the Quality of their (technical) Modeling by improving their technical Model [Q(Modeling)].

Moreover, decision parameters are not set in stone. So by asking WI-questions, Modelers become more aware of the Management Decision Consequences (MDCs), helping them to develop and simplify decision parameters to the most adequate, understandable and possible simplified form. Or as Albert Einstein quoted it:

"Everything should be made as simple as possible, but not simpler"

On the other hand, by asking WI-questions, Managers can study the effects of various decisions they might take in different (simulated future) circumstances (as roughly described by the Manager).

This process improves the decision taking skills of a Manager and therefore improves the Quality of the Decisions taken by Managers [Q(D)] in general. At the same time, the Modeler may use the given information from the Manager to improve his Model and (future) data as well.

Conclusion
We may conclude that the answer to the question 'What managers, who are not modelers, need to know about models' is:

Nothing, as long as Manager and Modeler intensively communicate with each other, ask WI-questions, are not afraid to admit their weakness or doubts, challenge each other and don't manipulate each other!

Perhaps an ever more tricky question to answer is:

"What must managers who are also modelers know about models?

Possibly Dave Ingram has the answer to this question....

Aftermath What happens when communication between Managers and modelers fails, is well illustrated in the Gulf of Mexico Oil Disaster, where BP CEO Tony Hayward stated before congress:
- “I simply wasn’t involved in the decision-making.”
- “Clearly an engineering judgment was taken.”

It's easy to spot failing Management-Modeler relationships by means of the next 'Management-Modeler Symptoms Matrix'.....

If you happen to be a modeler in the upper left quadrant, get out as fast as you can!