Understanding Confidence Levels in Time

What's the right understanding of the concept of 'confidence level' for a financial institution?

That's not an easy question....

A short (popular) definition of confidence level in terms of Solvency and Basel regulation would be:

The probability that a financial institution doesn't default within a year.


In this blog I'll discuss and compare three more or less accepted confidence levels (CFLs):

1. Dutch Pension Funds: CFL= 97.5% 
2. Life Insurers (Solvency II): CFL = 99,5%
3. Banks (Basel II/III): CFL = 99.9%

Understanding Confidence Level
Before we get into the details, let's first shine a light on a widespread misunderstanding regarding the concept of 'confidence level'.

To make the concept of confidence level more understandable, one might argue as follows:

1. The confidence level of a Dutch pension fund is defined as 97.5%
2. This implies that there's a one years probability that the pension fund has an one year default probability of 2.5% (= 100% - 97.5%)
3. This implies that the pension fund on average defaults once every 40 years (= 1 / 0.025)

This method of reasoning is completely

WRONG

The mistake that's been made is more or less the same as the next two fallacies:

1. If one ship crosses the ocean in 12 days. 12 ships will cross the ocean in one day
2. I fit in my jacket, my jacket fits in my suitcase, therefore I fit in y suitcase

Explanation
The probability of a pension fund with a confidence level of 97,5% going default, can be approximated by a simple Poisson distribution as follows:

From this we can conclude:

• In 40 years the pension fund has a 63% default probability.
• The probability that the pension fund defaults more than once is 26%
• The probability that the pension fund defaults exactly once in a 10 years period is 19.47% 

Insurer Confidence Level
For an insurance company with a confidence level of 99.5% the results are:

So even an insurer has a 4.88% default probability in a 10 years period on basis of a 99.5% confidence level. Keep this in mind if you take out a life insurance policy!!!


Banking Confidence Level
It starts getting serious when it comes down to a 99,9% confidence level for banks:

Comparison
Comparing the default probability of (Dutch) pension funds, insurers and bank on the long run:

Finally
Although this blog gives some more insight about the consequences of confidence levels on the long run, the real question of course is: what's the price you have to pay to avoid default risks?
That's something for another blog.....


Sources/Links
- Spreadsheet with tables used in this blog

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)

Soft-Risk Management

Never heard of of Soft-Risk Management? After this blog you'll never forget!

Google
This month Google's world class co-founders Page and Brin announced (SEC filing) they'll sell 17% of their shares (at today’s prices valued at $5.5 billion) in the next five years.

As a consequence their voting rights will be reduced to 48%, implicating they will no longer have a majority control. “They are both as committed as ever to Google...”, Google said in an e-mailed statement.
Why this statement? Was there anyone who doubted this?

Of course Google is still and will hopefully stay a strong company and a strong brand. Nevertheless - without jumping the conclusions - it's clear that this low-key announcement, although it doesn't seem to have any direct financial consequences, might turn out to be the straw that breaks the camel's back in Google's life cycle development. This kind of company press release is in fact a 'disguised risk indicator', or in other words a :

Soft-Risk Indicator (SRI)

A SRI may be defined as 'knowable' information about a company, that could influence the company's value now or in the future , but doesn't seem to have enough (financial) power to do so now or on its own

Although just one ignored SRI could already be fatal, a combination of two or more SRIs could become a severe risk. A bunch of SRIs could create a chain reaction and lead to a kind of supernova explosion.
It's just like a grain dust explosion. A few grains are no risk, they don't explode. However in an accumulation of grains, one innocent 'hot' grain or a small environmental change in dust concentration, is enough to create a mega explosion. Just like grain dust, SRIs can become a severe risk when the environment (suddenly) changes.
Consequently, an out of the blue 'change of environment' is also a Soft-Ris Indicator on its own.

Don't mix up Soft-Risk with Systemic Risk. Dust particles don't directly 'participate' in one another, in fact they build up to a certain critical density. Soft Risk Loss
SRL = E( SRI_i=1,2..n )It's just the composition of SRIs in combination with the special SRI of 'the change in environment' that creates a major accumulated (explosion) Soft-Risk that may eventually result in a Soft Risk Loss (SRL). However, once the SRL has occurred and has been measured, the corresponding SRI becomes a 'normal' Risk parameter.

Are there more Google SRIs?
Yes! One of the best Soft-Risk Indicator blogs of 2009 is written by Googles leaving lead visual designer Doug Bowman, it's called:

Goodbye, Google

Please read the next extract of Bowman's blog from a risk management perspective, as he explains his decision to leave Google after three years.

- 20 Mar 2009 -

Goodbye, Google

Without a person at (or near) the helm who thoroughly understands the principles and elements of Design, a company eventually runs out of reasons for design decisions. With every new design decision, critics cry foul. Without conviction, doubt creeps in. Instincts fail. “Is this the right move?” When a company is filled with engineers, it turns to engineering to solve problems. Reduce each decision to a simple logic problem. Remove all subjectivity and just look at the data. Data in your favor? Ok, launch it. Data shows negative effects? Back to the drawing board. And that data eventually becomes a crutch for every decision, paralyzing the company and preventing it from making any daring design decisions.

Yes, it’s true that a team at Google couldn’t decide between two blues, so they’re testing 41 shades between each blue to see which one performs better. I had a recent debate over whether a border should be 3, 4 or 5 pixels wide, and was asked to prove my case. I can’t operate in an environment like that. I’ve grown tired of debating such minuscule design decisions. There are more exciting design problems in this world to tackle.

I can’t fault Google for this reliance on data. And I can’t exactly point to financial failure or a shrinking number of users to prove it has done anything wrong. Billions of shareholder dollars are at stake. The company has millions of users around the world to please. That’s no easy task. Google has momentum, and its leadership found a path that works very well. When I joined, I thought there was potential to help the company change course in its design direction. But I learned that Google had set its course long before I arrived. Google was a massive aircraft carrier, and I was just a small dinghy trying to push it a few degrees North.

I’m thankful for the opportunity I had to work at Google. I learned more than I thought I would. I’ll miss the free food. I’ll miss the occasional massage. I’ll miss the authors, politicians, and celebrities that come to speak or perform. I’ll miss early chances to play with cool toys before they’re released to the public. Most of all, I’ll miss working with the incredibly smart and talented people I got to know there. But I won’t miss a design philosophy that lives or dies strictly by the sword of data.

The resemblance between Google and the financial sector is striking.
Can you see it?

Simply replace the next words in the above 'Google, Goodbye' article:

Google => X-Bank, Engineer => Accountant, blue => risk strategy
Design => Risk, border => uncertainty, pixels wide => promille

To help you, just press the next 'replace button' to change the text in the article and read the text again. This looks astonishing familiar, doesn't it?

Replace

More Soft-Risk Indicators
Bowman's blog makes clear that there's another Soft-Risk Indicator, called:

Data Decision Tunnel Vision

• Every decision in only based on data and models.
• Intuition and Fingerspitzengefühl are banned.
• Craftsmanship is not respected, but must be proved in detail with evidence based on facts and data.
• Possible events that can't be translated into (financial) data are not recognized as risk and are ignored.
• Events that don't fit into the data model are reformed until they do fit in
• Micro management confines the development of a helicopter view on the main risks

Although the list of Soft-Risk Indicators is endless, I'll try to list some common examples (mail me if you have more SRIs examples).

Examples of SRIs

• Frequent or unexpected change of CEO or other board members
• Unexplainable or untimely Actuary or Accountant change
• Intentions of board members not in line with policy
• Too good to be true revenues, profits, reporting or communication
• Delay in reporting or publishing
• Lack of transparency
  
• Conflicting statements or publications
• Main (unexplainable) shareholder changes
• Over-explaining by board members
  
• Unexpected main reallocation of assets
• Vacancy or Recruitment stop; Reorganizations
• A company takes extremely more risk after a HQ-Risk Analysis
• Increasing customer dissatisfaction
  

Soft-Risk or Risk?
Most of the SRIs are not present or recognized as Risk in our models. Why? Simply because SRI losses are not in the data we analyze. This could be (1) because of the very low occurrence probability of a SRI loss (the loss simply didn't occur yet), or (2) because most of the SRIs aren't identified as SRI or Risk at all, as they simply do not exist yet. Just like a sleeping virus, they might come into Risk Existence on basis of (unknown) future (environmental) changes.

The key difference between 'Risk as we now it' and a SRI is that a SRI is by definition 'not measurable'. SRIs manifest themselves directly in practice as a (non-directly traceable) loss occurrence.

VaR Models fail
This also implicates that our traditional VaR models are definitely wrong, because they only include 'risks of the past' en no 'future risks', e.g. Soft-Risks. These VaR-models significantly underestimate the risk in the tail.
Problem is that as VaR-probabilities are getting smaller and smaller (0.5% or less) it also gets increasingly more difficult to prove the models are right. Consequently the VaR-model loses his power.
Backtesting and recent studies show that we ought to be able to identify most bad VaR models, but the worrying issue is that we can't find any good models, moreover because SRIs are not in the model.

Denying Soft-Risk Indicators: The Meltdown
You might think 'Who cares about SRIs if you can't measure them?". Well, let's see what happens if we deny Soft Risk Indicators.

The most likely dead-end meltdown scenario of denying Soft-Risk Indicators goes something like this:

• The first years of a company's life is a race for revenues. Risk Management is on the second plan, as there's little to lose.
  
• After a few years revenues and profits grow, but become vulnerable and volatile. A new Board is appointed and a Risk Management Plan (RMP) comes in place to stabilize and improve results and to guarantee continuity.
• After the RMP has shown fantastic results for some years, some strange unexpected serials of events (SRIs) happen. The Board consciously discusses the effects of these events and concludes their company's results are not infected by the events. Moreover, company results are better than ever and the company's RMP has proven to be (Titanic) watertight.
  
• To be sure and transparent the Board checks its conclusions by ordering an external risk audit. The external auditor is just as biased as the Board and confirms the Board's conclusions: RMP is O.K.!
  
• Suddenly there's a totally unexpected big accident, a substantial one of loss. At first things still look under control, but soon the situation takes over. The board is no more in control. The company is lost.
  
• Soon all stakeholders are flabbergasted. How could this happen?!

Well it's clear, what happened is that the Board misinterpreted and neglected early warning signs and SRIs, resulting in a company meltdown.

How to prevent a melt down?
To prevent a situation like the one above, the board should

1. Set up a SRI-Register
   
2. Order the RM-Department to include SRIs in their risk model
3. Discuss the integral SRI-register monthly in the Board meeting
4. Interprete the SRIs, and take proactive actions to prevent the SRIs from becoming critical. This is Board's Craftsmanship!

As continuity is a company's main goal, managing uncertainty is the Board's main responsibility.

Redefining Risk
Once we realize that Soft-Risks are crucial in Risk Management, how can we include them in our Integral Risk Model (IRM)?
First we'll have to redefine Integral Risk as follows:

(1) I-Risk = Integral Risk = Measurable Risk + Unmeasurable Risk
(2) I-Risk = Integral Risk = Hard Risk + Soft Risk
(3) I-Risk = ∑( Threat_j x Vulnerabilityj x Cost_j ) +∑ E(SRI_i=1,2..n)

Keep in mind that the Integral Risk is not a number, as the SRL is not measurable. If you can't force your brain to 'quantum think' this way, just imagine the Integral Risk as the total company value (at stake).

Cleaning up
First 'cleaning up' action we can do is to investigate the relationship (correlation, covariance matrix, etc.) between each past assumed Soft-Risk event and the Vulnerability of each Hard Risk event. This tells us probably something of the influence (correlation) of certain (combination of) SRIs on the traditional Hard Risk parameters.

Probably this research will show that some of the SRIs could even be defined as Hard Risk variables. Unfortunately this investigation - as explained -won't tell us anything about the real unmeasurable Soft-Risks. The problem remains.....

Managing Soft-Risk
The real main problem is : If you can't measure Soft-Risk, how can you be sure your 'Soft-Risk Management' (SRM) is successful, as you can't measure the effects of your actions either?

This seems to be an insolvable problem. Insolvable because of what Bowman in fact calls our 'mono data mind set'. We are not trained in taking decisions without data. As we are not trained, we become unsure. Unsure about the risk of the impact of our decision, that is unmeasurable as well. Full circle, we're back where we started.

However, there's a way out of this paradox, it's called

Principle Based Risk Management

Before we dive deep, let's first take a step back and have a look at two important actual developments, (1) the Global Warming Problem and (2) Solvency II.

(1) Global Warming Problem
During recent decades scientists have developed different global warming models that contradict each other. The real climate is far too complex to be modelled. We could spend millions of dollars on research to find the ideal model, we will never succeed!

Step by step the leaders of this world recognize that they'll have to manage the global warming in a different way. It's no longer important whether or not there exists a provable global warming problem. The main question is whether we are willing to live up to the principle: "You don't foul your own nest"

This way of principle-based thinking requires reflection on the level of 'spaceship earth', on a 'global' level. However, simultaneously, it urges for acting in line on a 'local' level.

Although related with The Precautionary Principle, Principle Based Risk Management is much more fundamental. It's an adequate tool for fighting Soft-Risks.

(2) Solvency II
In our aim to strengthen the insurance industry solvency, implementation of Solvency II bears the a risk of an overshoot. Instead of managing risks first and in a better way, we translate every risk into capital requirements, consequently increasing the cost of doing business and insurance premiums. It's the perfect example of putting the cart before the horse. Although we expect Solvency II measures to work out in a better solvency, in reality we don't know, as this 'capital-increase scenario' hasn't been tested before and can't be tested. The presumed positive effect could just as well be adverse.

In our aim to avoid risk, we've created another additional risk. A risk we can't measure (yet). Yes, unfortunately, Solvency II is a SRI as well.

Instead of making Solvency II obligatory, a far more effective Principle Based response from the Regulator would have been:

"Prove us that you manage your own risks"

Back to Soft-Risk Management
It's not that difficult managing Soft Risks Principle Based. In fact we all have experience with Soft Risk Principle Based decisions when we decided to have friendship, marry, or to have a child. Or did you calculate the 'lifetime present value' of your child?

Try to apply the above principles in your own company or in your own department. Just start by investigating your Soft-Risk Indicators and start managing soft and hard risks Principle Based.

What principles can we formulate to manage Soft-Risk?

Well actuarial folks.... that's food for another blog as this blog is getting far too long..... O.K. .... I wont keep you waiting, just one Principle Based one-liner that tackles a whole bunch of SRIs at once

Bonuses are only paid in case of
High Customer Satisfaction

Related (additional) Sources:
- Unmeasurable measures: The lawlessness of great numbers
- The Risk Equation
- An Additional Way of Thinking... :The Quantum Perspective
- From Principle Based Risk Management to Solvency Requirements
- Measuring the unmeasurable
- Managing Extraordinary Risk (2009, Towers Perrin)
- Measuring the Unmeasurable: Balanced Scorecard
- NYT: Risk Mismanagement
- Backtesting Value-at-Risk Models (2009)
- Quality control of risk measures: backtesting VAR models
- Metrics: Overmeasuring Our Way to Management

Control Leverage

Key question is whether 'adding more control' will stabilize financial institutions like banks, insurance companies or pension funds.....

With all the - apparently failing - new legislation of the last decade already in place and new control measures like Solvency II and the strengthening of the Basel II Framework ahead, one might - at least - question whether we're on the right track with this intensified 'control approach'.

Will adding more control
empower or paralyze financial institutions?

In other words: Is the Control Leverage Effect positive or negative?

Insurance
In an FT-Adviser article called 'Solvency II costs are unsustainable', Joy Dunbar reports that the ABI (Association of British Insurers) has warned that the costs of implementing Solvency II regulations could destabilize the industry across Europe.

To gain more control (financial stability), European Insurers are obliged to implement Solvency II measures by the end of 2012, starting already in 2010.

Impact Solvency II
The increasing control costs and capital demands of Solvency II will have an enormous impact om the insurance market:

• Recapitalization: Insurers need to acquire fresh equity capital (billions of Euros) in the market
  
• Over-Capitalization: More 'dead' capital is created in financial institutions, resulting in declining investment returns in insurance.
• Market shake out: Companies will exit the market
• Pricing effects: prices (premiums) will rise, cover will be reduced

Banks
Whereas the European Insurers are on a more or less 'blind track' with regard to the implementation of Solvency II, the banks - according to chairman of the Basel Committee Mr Wellink - stressed that "decisions on the final proposals and their calibration will be made only after a thorough analysis of the impact assessment and the comments received on the consultative documents. The Committee will ensure that implementation of the new standards is consistent with financial market stability and sustainable economic growth".

The real problem
One doesn't have to be an actuary or financial expert to conclude that we're at the end of the road where adding more of the same type of control measures will substantially stabilize our system.

Without diving deep into real life quantitative analyses, let's get a helicopter-view and take a look at an average 'Control-Return Matrix' to do some 'rule of thumb' exercises...

Rule of thumb Control-Return analyses

Phase I
A few decades ago, starting in the good old sixties of the twentieth century, there where only limited control measures in place (control=0). The average Return on Equity (ROE) of a company was (e.g.) 6% and although Value at Risk (VaR) didn't yet exist as such, the 6% ROE could easily swap between (e.g.) +15% and -50%.

Financial markets where not that developed as today (no derivatives, , CDS, etc). Systemic risk was almost non-existent and accounting principles where based on the simple and relatively stable method of 'historical cost'.

The need for 'more control' was clear to everybody. More control implied lower costs, 'more opportunity insight' and 'more risk control'.
More control turned out to be a good investment and would lead to realizing a better return (ROE) in combination with a lower risk (Var) and a higher 'upward potential'. Every stakeholder was happy.

Phase II
Getting into the eighties and nineties of the twentieth century, 'control' had done its major job and still did, as it was able to manage the few relatively small recessions in those years.

With the help of the oncoming heavy computers, the first baby steps regarding new risk management techniques and ALM (Asset Liability management) were taken.

This way major risks (VaR) could further reduced, sometimes at the cost (expense) of a small reduction of the ROE. But this small effect was largely compensated by the 'fallacy high returns' in the high trust market.

Phase III
At the beginning of the Twenty First Century a new recession made clear the financial environment had substantially changed:

• New techniques, models and the use of modern computer software led to new markets and new products like derivatives
• Markets became global, (on face) transparent, in open competition
• A lack of insight with regard to systemic risks
  
• Differences in local supervision, legislation, administration and accounting rules, led to a complex, non-transparent global market.
• In order to be able to compare companies, they had to be valued at 'market value', implicating the birth of more volatile (stock) markets....
• Step by step, the public and media became more conscious. Investors and consumers understood that even if a 0.5% VaR level would be further reduced, it wouldn't make any sense because it would be always overshadowed by the non-trackable, nor manageable, risk of let's say 1 à 2%. And moreover, who would trust his money to a bank that would go bankrupt once every 50 or 100 years....
  
  

Investors, Boards, Managers, everyone lost their handrail....

In the recent decade (2000-2010) things got worse :

• Existing control and accounting systems would locally differ and failed to meet the complex demands of the new markets
  
• Supervisors en regulators, normally ahead of the market, were suddenly one step behind and unable to catch up given the actual system of supervision
  
• It had become clear that new financial products ( e.g. CDOs, CDSs, subprime mortgages, swaps, swaptions) had been introduced without a good understanding of their financial construction or risk
• Turbulence in the markets. Relatively stable stocks of big international firms, suddenly appeared remarkably unstable, due to new volatile markets/products and 'fair value accounting'.
• The once so well controlled VaR risk exploded, due to these new types of risk in the market, the fair value accounting principles and the spooky systemic risk.

Way out
Like everyone else - totally flabbergasted - supervisors and regulators immediately grabbed the traditional emergency brake of 'more control'.

Unfortunately, more 'traditional' control in phase III will not have the same effect as in phase I or II. The effects of more traditional control in phase III will be:

• Substantial but unsure decrease of ROE and 'upward potential'.
  The effects are not known upfront and can't be estimated well.
  Sure is that the costs of extra control and 'dead money' will have a negative impact on the ROE.
  
  
• Unknown and questionable reduction of VaR risks, as one thing is sure: the new type(s) of (VaR) risks can not be estimated by our retrospective based models. Probably, all efforts in vain, the remaining actu(ari)al risk level will not be substantially reduced.
  
  
• Trying to 'catch' more 'safe' risk levels (lower α , VaR) will lead to over-capitalization and 'dead' money in the balance sheet and an unbalanced growth of derivatives.
  
  

• The market of derivatives continuous to grow.
  
  The notional value of derivatives held by U.S. commercial banks increased $804 billion in the third quarter to $204.3 trillion.
  
  This, despite the statements of Fed Chairman Bernanke who says he wants to avoid the possible risk of a future speculative bubble.
  
  And despite of Treasury Secretary Geithner who says he wants to reform financial regulation to avoid a future debt disaster.
  
  
• Because the real issues of the financial crisis where not solved, but only covered up with government help (money), new uncontrollable 'bubbles' will keep showing up.
  

Solutions
probably the best solution is not 'more control', but

Other Control

Examples of 'other control' are:

• Obligatory report and central registration of all derivatives under one worldwide supervisory. This way systemic risk analyses won't be 'guess statistics' anymore and can be managed. System risk is one of the weirdest risks to tackle, as is illustrated by the next article:
  
  Why Your Friends Have More Friends Than You Do
  
  Although the Exchange Commission has taken some serious steps in 2009 to regulate and strengthen the over-the-counter ("OTC") derivatives, this process will probably not be rigorous and fast enough to prevent a possible new bubble or collapse.
  All OTC market products should be asap standardized on a centrally administered basis.
  
  
• Limit and control the derivatives market. Maximize the derivative market in respect to the 'normal' market. Limit each companies derivatives in line with his equity. New regulation should also be developed with regard to participating in non defensive (strange) derivatives (e.g. define max. exposure multipliers).
  If not the next bubble is a fact!
  
  
• New derivatives should be subject to approval ('no objection') by the regulator before market launch.
  
  

So it all comes down to the 'right control' leverage.
It's either positive leverage with 'new other control' or negative leverage with 'more of the same traditional control' and waiting for the next bubble. What do you prefer as an actuary?

Sources:
- Contagion in Financial Networks
- Testimony Concerning OTCs (Over-the-Counter Derivatives )
- OCC’s Q3 2009 Report on Bank Trading and Derivatives Activities
- The bigger and riskier monster....
- Tarp facts: The Troubled Asset Relief Program
- The Investment Fallacy

The Actuarial Black Eye

In his blog David Merkel gives a fabulous book review of the book:

While America Aged

The book and blog show that actuaries (and accountants as well) were not disciplined enough to resist politicians pressure and large companies board (and shareholder) short-term result demands. As a direct consequence those companies got into serious trouble.

Stick to one's guns, and keeping a save eye on the future, is one of the essentials of the actuarial profession.

Training (not just study alone) in giving the right push back on board level, should therefore be an obligate part of the education (and accreditation) of actuaries and accounts.

As (UK) Sir Derek Morris stated in his "review of the actuarial profession: interim assessment" (2004):

Too much has been expected of actuaries and, explicitly or otherwise, too much has been promised by them.

Clients have looked to actuaries to provide certainty, and actuaries have often appeared to provide it.

For Dutch actuaries, see also Willemse and Wolthuis in: "On the practical meaning of probability based solvency".

Actuaries are almost just like real human beings: after a few years successful studying and modeling, they gain confidence. They start to believe that reality will also act according their models. Moreover, they might get overconfident and think that their view and expertise on reasonably well predictable issues like life, death and disability are - with the same amount of certainty - also applicable on other issues like 'inflation' and the development of the 'stock market'.

This it typically a case of :

That what develops you, eventually might kill you

Practice hasn't shown that good actuaries are,by definition, also good weatherman.

The book also shows that self-regulating without clear targets and constraints is a fairy tale.

Keep in mind the Mongolian Proverb:

Of the good we have an understanding,
for fools we keep a stick upstairs

Success in being a PBA (Push Back Actuary)!