Pisa or Actuarial Compliant?

When we talk about actuarial compliance, we usually limit this to our strict actuarial work field.
In a broader sense as 'risk managers', we (actuaries) have a more general responsibility for the sustainability of the company we work for.

Compliance is not just about security, checks, controls, protection, preventing fraud, ethical behavior. Moreover  compliance is the basis of adequate risk management and delivering high standard service and products to your companies clients.

Pisa Compliant
No matter how brilliant and professional our calculations, if the data - on which these calculations are based on -  are 'limited', 'of insufficient quality' or 'too uncertain', we as actuaries will finally fail.

Therefore , building actuarial sandcastles is great art, however completely useless. Matthew 7:26 tells us :  it's a foolish man who builds his actuarial house on the sand....

And so, let's take a look if we have indeed become 'Pisa Compliant' by checking if our actuarial compliance is build on sand or on solid ground. In other words: let's check if actuarial compliance itself is compliant...nd.

Actuarial Data Governance
To open discussion, let's start with some challenging Data Governance questions:

• Data quality compliance
  How is 'data quality compliance' integrated in your actuarial daily work? Have you addressed this issue? And if so, do you just rely on statements and reports of others (auditors, etc), can you agree upon the data quality standards (if there are any). In other words: are the data, processes and reports you base you calculations on, 100%  reliable and guaranteed? If not, what's the actual confidence level of your data en do you report about this confidence level to the board?
  
  
• 
  Data quality Conformation
  Have you checked your calculation data  set on bases of samples or second opinions?
  
  And if so, do you approve with the methods used, the confidence level and the outcome of the data audit? 
  
  Or do you just 'trust' on the blue eyes of the accountant or auditor and formally state you're "paper compliant"?
  
  Did you check if  client information, e.g. pension benefit statement, are not only in line with the administrative data, but also in line with insurance policy conditions or pension scheme rules?
  
  
• Up to date, In good time
  To what quantitative level is the administrative data  'up to date' and is it transparent?
  
  Do you receive administrative backlog and delays reporting and tracking and if so, how do you translate these findings in your calculations?
  
  
• Outsourcing
  From a risk management perspective, have you formulated quantitative and qualitative demands (standards) in outsourced contracts, like 'asset management', 'underwriting'  and 'administration' contracts?
  
  Do you agree on these contracts, do 'outsourcing partners' report on these standards and do you check these reports regularly on a detail level (samples)? 

And some more questions you have to deal with as an actuary:

• Distribution Compliance
  Is the intermediary and are the employers and customers your company deals with, compliant? What's the confidence level of this compliance and in  case of partially noncompliance, what could be the financial consequences? (Claims)
  
  
• Communication Compliance
  Is communication with employees, customers, regulators, supervisors and shareholders compliant? Has your board (and you!) defined what compliance actually means in quantitative terms?
  
  
  
  Is 'communication compliance' based on information (delivery and check) or on communication?
  
  In this case, have you've also checked if  (e.g.) customers understood what you tried to tell them?
  
  Not by asking if your message was understood, but by quantitative methods (tests, polls, surveys, etc) that undisputed 'prove' the customer really understood the message.
  
  
  
  Effective Communication Practice
  Never ask if someone has understood what you've said or explained. Never take for granted someone tells you he or she 'got the picture'.
  
  Instead act as follows: At the end of every (board) presentation, ask that final and unique question of which the answer  assures you, your audience has really understood what your tried to bring across.

Checking Compliance

Now we get to the quantitative 'hard part' of compliance:

How to check compliance?

This interesting topic will be considered in my next blog.... ;-)

To lift a little corner of the veil, just a short practical tip to conclude this blog:

Compliance Sample Test
From a large portfolio you've taken a sample of 30 dossiers to check on data quality. All of them are found compliant. What's the upper limit of the noncompliance rate in case of a 95% confidence level?

This type of question is a typical case of:

“If nothing goes wrong, is everything alright?”

Answer.

The upper limit can be roughly estimated by a simple rule of thumb, called 'Rule of three'....

'Rule of three for compliance tests'

If no noncompliant events occurred in a compliance test sample of n cases, one may conclude with 95% confidence that the rate of  noncompliance will be less than  3/n.

In this case one can be roughly 95% sure the noncompliance rate is less than 10% (= 3/30). Interesting, but slightly disappointing, as we want to chase noncompliance rates in the order of 1%.

Working backwards on the rule of three, a 1% noncompliance rate would urge for samples of 300 or more. Despite the fact that research for 46 international organizations showed that on average, noncompliance cost is 2.65 times the cost of compliance, this size of samples is often (perceived as) too cost inefficient and not practicable.

Read my next blog to find out how to solve this issue....

Related Links:
- Actuarial Compliance Guidelines
- What Is The Right Sample Size For A Survey?
- Epidemiology
- Probability of adverse events that have not yet occurred
- The True Cost of Compliance (2011)
- 'Rule of three'
- Compliance testing: Sampling Plans (accounting standards) or Worddoc

Humor: Merkozy, It's too late

Comparing 5 year exchange rates USD/EUR (decline : 17%) and USD/CNY (decline 21%) clearly shows the negative outlook of the US Dollar.

Both U.S. and Europe, are facing severe debt problems they can not solve with more debt.

Desperate actions
President Obama tries to stimulate economy by creating 1.5 mln new jobs with a $ 450 billion investment (American Job Act).

In Europe president Merkel (Germany) and Sarkozy (France) have joint their strengths and totally different characters into 'one personality', to create not only a strong financial but also economic European union.

This new economic union is necessary to establish a firm grip on the measures that weak financial European countries like, Greece, Italy, Ireland, Spain and Portugal have to take to recover from their debt.

It's too late
Unfortunately there's no support for such an initiative. Unluckily, no Merkozy will be able to prevent Europe from a financial meltdown.

The  only way out seems a European split in relative strong and weak countries.

Make up your mind on the geographic spread of the assets of your company. Get out before it's too late....

Irrational Risk

Actuarial work is demanding..., so you're arriving late at your hotel that night. The hotel manager has only two rooms left. These two rooms are exactly the same, except for one aspect: The fire alarm.....

The manager tells you that in the event of a nighttime fire due to the usual causes, guests in Room 1, equipped with Alarm 1, have an actuarial calculated  2% chance of dying. Guests in Room 2, equipped with Alarm 2, have only a 1% chance of dying.

However - things in life are always complicated -  there's a slight problem.....

According to the manager...... The wiring of Alarm 2 is such that it sometimes causes electrical fires that increase the risk of dying in a nighttime fire by an additional 0.01%.

In other words, Alarm 1 is associated with a 2% risk of death and Alarm 2 is associated with a 1% + 0.01% (betrayal) risk of death.

What room do you choose as a professional actuary?

Outcome
According to a study by Gershoff and Koehler, most participants choose the room with Alarm 1. This,  even though this room 1 has double the increased risk of fire death, according the researchers. Reason: most participants found the tiny risk of "betrayal" (product malfunction) much more frightening than the much larger risk of actually dying.  When people get upset by a tiny risk, they often paradoxically choose the much larger risk.

Personally I think a more imaginable risk 'weighs' stronger than a non-specific abstract risk and in general people are unaware of conditional probability effects......

Conclusion

This simple example proofs that emotion has a strong influence on risk decisions.

Just like in our actuarial profession, risk decisions are often irrational.

It is our duty as actuaries to demystify and to rationalize risk. However, sometimes we're victim of the same emotional bias....




Read more about this interesting subject on:

- Vaccination and betrayal aversion (2011)
- Safety First? The Role of Emotion in Safety Product Betrayal Aversion (2011)

- The Risk-as-feelings hypothesis in a Theory-of-planned-behaviour
- Behavioral economics: the missing link in the financial crisis-  
- Decisions are Never Emotional

U.S. Debt Autopsy

Coming back from vacation, the world seems lost. You don't need to be an actuary to grasp that the recent decision to lift the U.S. debt ceiling is first class trickery and completely inadequate.

What the Chinese rating agency Dagong already concluded back  in November 2010, is only now (August 5, 2011) reluctantly and partly followed by S&P:

U.S. AAA status = Dead.

It's interesting to see which countries Dagong rates lower than  the three famous rating Agencies in the U.S.  (download complete Dragon report).

Meanwhile Dagong downgraded the U.S. again to an ordinary A-status on august 2, 2011.

The arguments for country degrades (as the U.S.) are as much clear as simple: if lifting debt ceilings is not at the same time combined with serious debt reduction measures (spending cuts), you go DOWN!

The outlook on the U.S. is still negative.

Outlook
Let's take look at what happened during 2011 and what 2012 will bring..

This chart instantaneously makes clear what's happening:

• Jan 2011 -  half May 2011
  Although  the whole world can figure out that the original debt ceiling of  $14.294 trillion will be reached within a few months, no measures or actions are taken by the U.S. Treasury to prevent a debt default,.
  
  
• May 16, 2011 - August 1,  2011
  Treasury Secretary Timothy Geithner informs Congress he will start tapping into federal pension funds on Monday to free up borrowing capacity as the nation hits the $14.294 trillion legal limit on its debt.
  
  By these and (possible) other optical actions the actual debt is kept artificially stable, slightly above the first ceiling. Of course the factual debt will (non reporting or visible) continue to keep growing.
  
  
• August 1&2, 2011
  The U.S. House of Representatives and the U.S. Senate pass the Budget Control Act on Aug 2, 2011.The debt ceiling is immediately raised by $400 billion, to $14.694 trillion.
  
  A second debt ceiling increase allows the current new ceiling to grow by an additional $500 billion, to $15.194 trillion, so that government can pay its bills until the end of February 2012.  However, Congress has the authority to reject this second increase.
  
  
• August 5, 2011 - March 2012
  
  TresuryDirect reports show the debt catch-up effects on August 5, 2011.
  
  Already $271 billion of the $400 billion debt ceiling lift turns out to be 'consumed'. Another $129 billion is left.
  
  As my two year old son can calculate: If no measures will be effective, around mid September 2011 a new ceiling crisis and media lift-show shall start.
  
  After agreeing in  September to the second ceiling of  $15.194 trillion the muppet debt show will start again in March 2012, when the second ceiling will break.

The party is over
I'm not a pessimistic person by nature, but the U.S. is running out of possible solutions. 

It looks like the financial space flight program is over. 

We'll have to build a society on new ethical financial principles.

If real measures stay out and claims on other countries or banks (as was the case with the sub prime debacle) are limited, the U.S. will unfortunately default in the end.

This U.S. default will take along most western countries.

It will result in a worldwide financial meltdown.

They only way out that seems left is:

Inflation

Let's hope for the best or a miracle. God bless America!



Related Links and Resources:
- Spreadsheet (Excel) with 2011 Debt Data
- S&P Report, August 5, 2011
- TreasuryDirect (U.S. Debt development)
- Debt Ceiling Increase of 2011
- Alert - Just So We Don't Get Confused As To The Source Of Our Little Problem

Humor: Actuarial Mind

In July 2011 holidays  - instead of blogs - are ahead...

Just chew this month on the next actuary no-brainer:

The smartest actuary in the world

The Pope, a well seasoned actuary and a student nurse are flying on an airplane. The captain comes back and says that he has some bad news and some really bad news. The bad news is that the plane is going to crash! As he puts on a parachute and jumps out he says that the really bad news is that there are only 2 more parachutes.

The actuary says: “I am the smartest man in the world. I've just calculated my life expectancy to be more than fifteen years. Excuse me...” With that he puts on a parachute and jumps out.

The Pope says: “Well, my child, I would love to live, but I believe that my time is up. Please take the other parachute and save yourself.”

The student nurse says: “Not to worry Holy Father. Right now the smartest man in the world is trying to find the rip-cord on my back pack!”

Impact or Probability?

We all are more than familiar with the definition of Risk:

Risk = Probability × Impact= P×I

This way of measuring risk is a nice, simple, explainable and intuitive way of ordering risks in board or bath rooms, but unfortunately quite useless.

To demonstrate the limits of this kind of typical Risk definition, let's take a look at the next story:

The Risk of bicycling

You decided to start a 3 year math study at City University in London. From your brand new apartment in Southall, it's a 12.5 mile drive to the University at Southhampton Street.  As a passionate cyclist you consider the risk of cycling through London for the next three years.

Based on your googled " DFT's Reported Road Casualties 2009" research (resulting in a cycling death rate of 36 per billion vehicle miles), you first conclude that the probability of getting killed in a cycle accident during your three year study is relatively low : 0.1% (≈ 3[years] × 365[days] × 25[miles] × (36 [Killed]  ÷ 10⁹[vehicle miles]).

Subjective probability

After this factfinding you start to realize it's YOU getting on the bike and it's YOUR 0.1% risk of DYING  in the next three years of your study....

Hmmmm...this comes closer; it makes things a little different, doesn't it? 

Its looks like 'subjective probability' - on reflection - is perhaps somewhat different from 'objective probability'.

While your left and right brain are still in a dormant paradoxical state of confusion, your left (logical) brain already starts to cope with the needs of the right (emotional) half that wants you on that bike at all costs!

Russian Roulette

Now your left brain tells you not to get emotional, after all it is 'only' an additional 0.1% risk. Already your left brain starts searching for reference material to legitimate the decision you're about to take.

Aha!.... Let's compare it with 'Russian Roulette', your left brain suggests. Instead of 6 chambers we have thousand chambers with one bullet. Heeee, that makes sense, you talk to yourself.

With such a 1000 chambers Russian gun against my head I would pull the trigger  without hesitating....  Or wouldn't I?..... No.., to be completely honest, 'I wouldn't risk it', my right brain tells me.

Hé... my left brain now tells me my right brain is inconsistent: It wants me on the bike but not to take part in a equal 'death probability game' of Russian roulette. Why not?

In Control
My left half concludes it must be the 'feeling' of my right side that makes me feel I'm 'in control' on my bike, but not in case of Russian Roulette. That makes sense, tells my left brain me. Of course! Problem solved! My right and left brain finally agree: It's only a small risk and it's I who can control the outcome of a healthy drive.  Besides, this way the health benefits of cycling massively outweigh the risks as well, my right brain convinces me superfluous.


A final check by my right brain tells me: If I can't trust myself, who can I?
This rhetorical question is the smashing argument in stepping on the bike and to enjoy a wonderful ride through London City.
As ever...,

My left brain is always right!

Aftermathematics
After returning from my accidentless bike trip, I enjoy a drink with a colleague of mine, the  famous actuary Will Strike  [who doesn't know him? ;-)].


After telling him my 'bike decision story' he friendly criticizes me for my non-professional approach in this private decision problem. Will tells me that I should not only have analyzed the probability (P), but also the Impact (I) of my decision. Remember the equation: Risk=P×I?

Yes of course, Will is right. How could I forget? ..., the probability of getting a deathly accident was only 0.1%.

Yet, 'when' a car hits you full, the probability of meeting St. Petrus at heaven's gate is 100% and the Impact (I) is maximal (I=1; you're dead ...)

Summarized:

Risk[death on bike;25 miles/day; 3 years] =
Probability × Impact = 0.1% × 1=0.1%

From this outcome it's clear that, even though the Impact is maximal (1=100%) , on a '0% to 100% Risk scale' this 3 year 'London-Bike Risk Project' seems negligible and by no means a risk that would urge my full attention.

I'm finally relieved... it always makes a case stronger to have a taken decision verified by another method. In this case the Risk=P×I method confirmed my decision taken on basis of my left-right brain discussion.  Pff....

Afteraftermath
The next morning, after my subconscious brain washed the 'bike dishes' of the day before, I wake up with new insights. Suddenly I realize I tried to take my biking decision on the wrong variable: Probability, instead of Impact.

Actually, in both cases and without realizing, I took my decision finally on basis of the Impact and the possible 'Preventional Control' (not damage control !!!) I  could exert before and during my bike trip.

I had to conclude that in cases of high Impact (I>0.9), nor my left-right brain chat, nor the 'Risk=PxI' formula lead to a sound decision, because both are too much based on probability instead of Impact. In other words:

In case of high Impact, probability is irrelevant

In case of high Impact, only control counts

From now on this 'bike conclusion' will be engraved in my memory and I will apply it in my professional work as well.

P.S. for disbelievers, the tough ones!
If you're convinced you would take the risk of firing the 1000 chamber  Russian gun against your head, you probably valuate the fun of the bicycle trip higher than probability of the loss of your life or good health.

In this case, suppose someone would offer you an amount of money if you would take part in a 1000 chamber Russian roulette instead of a bicycle tour. At which amount would you settle?

Let's assume you would settle at € 10.000.000 (I wouldn't settle for less). In this case you really value your bicycle trip!!!! 

As we've seen in banking business as well : extreme low probabilities and high impact situations are tricky! That's why stress tests focus on impact and not on probability.

The different faces of Risk
Another issue when looking at risk is that risk is always conditional.
'The' probability of death or 'the' mortality rate doesn't exist. Mortality depends on a number of variables, such as age (the run down state of your DNA quality), the DNA-quality you where born with and lifestyle. Secondly mortality also depends on a number of uncertain events in your life.

To demonstrate this 'Chameleon property' of probability, lets take a look at the probability of a meteor hitting good old earth.

The initial probability of an asteroid devastating the earth within a 10 year time frame is around 0.1%. A typical case of low probability and high impact. Once we've become aware of a spotted meteor in our direction, the probability suddenly changes from a general probability in a time frame to a asteroid specific probability during his actual passage of the earth.
In case of  the asteroid '2011 MD'  that will pass the earth at a minimal distance of 11000 km on June 27, 2011, this specific probability turns out 0.11% (remember the Russian Gun...).

With a diameter of around 8 meter, this asteroid is no big threat to our civilization.

Here's a short impression what's coming flying in on us within the next decades (Source: Nasa; asteroid>50 meter or minimum distance< 100,000km):

Apart from some 'big asteroids' in the next decade, this picture puts our minds at rest. Yet we should keep in mind that most asteroids are discovered only a few weeks before a possibe collaps...


Risk Maps
A nice example of the limits of the Risk=P×I model in combination with a nice aleternative, is demonstrated by Fanton and Neil in in a document called: 'Measuring your Risks: Numbers that would make sense to Bruce Willis and his crew'.

In  their document they analyze the case of the film Armageddon, where an asteroid of the size of Texas is on a direct collision course with the earth and  Harry Stamper (alias Bruce  Willis) saves the world by blowing it up.

Trying to fill in the Risk=P×I model in this Armageddon case is useless.

In this case, Risk is defined as:

Risk =  [Probability of Impact]  × [Impact of asteroid striking the earth]
 
Fanton and Neil conclude:

• We cannot get the Probability number.
  The probability number is a mix up. In general it makes no sense and it's too difficult for a risk manager to give the unconditional probability of every ‘risk’ irrespective of relevant controls, triggers and mitigants.
• We  cannot  get  the  Impact  number. 
  Impact (on what?) can't be unconditional defined without considering also the possible mitigating events. 
• Risk  score  is  meaningless.
  Even  if  we  could  get  round  the  two problems above, what exactly  does  the  resulting  number  mean?  
• It  does  not  tell  us  what  we  really  need  to  know. 
  What  we  really  need  to  know is the probability, given our current state of knowledge, that there will be massive loss of life.

Instead of the Risk=P×I model,  Fanton and Neil propose (Measuring risks) the use of  causal models (risk maps) in which a risk is characterised by a set of uncertain events.

Each of these events has a set of  outcomes and the  ‘uncertainty’  associated  with  a  risk  is  not  a  separate  notion  (as  assumed  in  the  classic approach).
Every event  (and  hence  every  object  associated  with  risk)  has  uncertainty  that  is characterised by the event’s probability distribution.

Examples:

The Initial risk of meteor strike
The probability of loss of life (meaning at least 80% of the world population) is about 77%:

In terms of the difference that Bruce Willis and his crew could make there are two scenarios: (1) the meteor is blown up and (2) where it is not.

Reading off the values for the probability of “loss of life” being false we find that we jump from 8.5% (meteor not blown up) to 82% (meteor blown up). This near tenfold increase in the probability of saving the world clearly explains why it merited an attempt.

Lessons learned
Use (Bayesian) Risk Maps rather than the Probability Impact Model or Risk Heat Maps, if you want to take decisions on facts instead of your intuition.

P.S. Many thanks to Benedict Escoto, who attended me on a wrong interpretation of the bicycle risk on bases of the Biomed info.
See document: Deaths of Cyclists in London: trends from 1992 to 2006
I rewrote this blog on information of DFT.

Related Links:
- DFT's Reported Road Casualties 2009
- Pedal cyclist casualties in reported road accidents: 2008 
- Is Cycling Dangerous?
- Cycling in London – How dangerous is it? (2011)
- Nasa: Small Asteroid to Whip Past Earth on June 27, 2011
- Nasa: Close (future) asteroid approaches...
- Nasa: Differences between Asteroid, Comet, Meteoroid, etc.
- Nasa: Search asteroid approaches in data base
- Nasa: Impact Probability of asteroids 
- Fanton & Neil: Measuring risks
- Fanton & Neil: Bayesian networks explained (pdf)
- Neil: Using Risk Maps!

Adds:
Using Risk Maps


Deathly bike accedents in London

Actuary Garfield

There's not a lot of 'Actuary Humor' on the Internet. Here's one...

Actuary Garfield explains how actuaries think...

Great and lots of humor, those Garfield cartoon strips, (especially those about actuaries....).

Original Sources:
- Garfield Snow
- Garfield Snowman

Short Term Longevity Risk

As well-born actuaries we all know the long term risks of longevity:

• Life Expectancy variance Monster
• Longevity Swaps: The Next Bubble
• Coverage Ratio Solution Space
• Exceptional Longevity Predictable

Lots of actuaries keep expending their energy on calculations of 50 years ahead mortality probabilities....  And indeed..., this is challenging....

Some research reports predict a decline in life expectation, others and more serious recent reports show a steady increase of life expectation.

Mission Impossible

Fact of actuarial life is that - although long term research is useful and educational - we are no Actuarial Magicians.

We should never suggest that we're able to value a bunch of complex and systemic risks  (liabilities, assets,mortality, costs, demographics, etc) into a reliable consistent model that predicts reality.

It's a farce!

What CAN we do?

Instead trying to compress a complex of long term risky cash flows into one representing unique value, we need to:

1. Analyze and model the short term risks
2. Develop a method (system) that enables boards of directors to manage and control their risky cash flows (profit share systems, experience rating, etc.).

Example: Short Term Longevity Risk

As a 2011 report of the National Research Council clearly shows:  The previous 50 years we've seen a 3 months yearly increase of lifespan every calendar year.

Instead of recalculating, checking and pondering this trend, let's take a look at the short term effects of this longevity increase trend.

Effect of 'one year life expectancy' increase 
First we take a look at the cost effect of the increase of 'one year of life expectancy' on a single-premium of a (deferred) life annuity (paid-up pensions)...
( Life table total population: United States, 2003 )

Depending on the discounting interest rate, a one year improvement of longevity for a 65 old person demands a 2,3% to 4,0% increase of the liabilities.

Of course the increase of the liabilities of a portfolio (of a pension fund) depends on the (liability weighted) age distrubution of the corresponding portfolio.

Here's a simple example:

This comes close to the rule of thumb as mentioned by AEGON:

10% mortality improvement adds one year to life expectancy, and one year of life expectancy adds 4% to the required value of a pension fund’s reserves

Conclusion

From the above presented visual sensitivity analysis we may conclude that for general (distributed) portfolio's a 'one year lifetime increase' will demand approximately 4-5% of the actual liabilities.

A three to four months yearly longevity-increase - as is still the actual trend - will therefore demand roughly a substantial 1,5% (yearly) of the liabilities.
This implies that in case your contribution is calculated at 4% and your average portfolio return is 7%, there's 3% left for financing longevity and indexation (=method). As 'longevity growth' in the near future will probably cost about 1,5%, there's  only 1,5% left for indexation on the long run.


Case closed


Related links:
-  Spreadsheet (xls) with data used in this blog
- Forecasting longevity of Dutch pension scheme members using postcodes
- Increasing life expectancy at pension funds (uvt;2011)
- Life Tables for the United States Social Security Area 1900-2100
- Valuing Pension Fund Liabilities on the Balance Sheet
- No limits to life expectancy?
- Broken Limits to Life Expectancy
- NRC: Explaining divergent levels of longevity (pdf;2011)
- Wolfram Alpha: Longevity U.S.
- AEGON: Longevity Rule of thumb

Google Hits on Actuary

Google can be a great help for actuaries. Especially 'Google Insights' and 'Google Trends' are two useful applications for retrieving relative Google Search Hits data from the Internet.

Google Insights Example
Let's dive a little deeper into Google Insights and start with researching the relative development of the number of hits on the word 'Actuary'.
Here is the result (period 2004-2011-May, extracted csv-file, Excel-Graph):

Explanation
The numbers on the graph reflect how many searches have been done for a particular term (e.g. 'Actuary'), relative to the total number of searches done on Google over time. They don't represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. Each point on the graph is divided by the highest point, or 100.

Conclusion

Clear is that the search for (the word) actuary is relatively declining from 2004 to May 2011.

To keep the actuarial profession virtually alive we'll need to make more noise as actuaries on the Internet.

Step outside, spread the (acturial) word, make yourself visible in the outer world and let people wonder:  'who's that?',  'what a professional', 'what's his job?', 'Actuary?', 'I will google it!'.

So let's Twitter and Blog to get more actuarial exposure...


Actual Data

Apart from generating these kind of relative time-data, Google Insights can generate actual data anywhere on any web-application or presentation.

This way your data will always be up to date!
Moreover Google Insights is easy to handle without any code knowledge.....


Some examples....

(1) Actual relative development of the number of hits on the word 'Actuary'


(2) Top searches and rising searches on Google for the word 'Actuary'

More applications

The next example shows how you may use Google Highlights as a market crash predictor.  

It turns out that in advance of the 2008 market crash, Google searches on "Stock market crash" increased...

Make you own discoveries, highlights or trends (e.g 'Solvency II') and enjoy!


Related Links:
- Actuaries on Twitter
- Google Insights 
- S&P 500 Data 
- How Google Trends and Internet Searches Correlate with Asset Prices
- Google trends: 21 May 2011: End of the world, predicted by Harold Camping

Actuarial Proverbs: Will Europe Survive?

According to Eurostat, Europe - especially the Euro (€) 'Coin' Countries that put all their Euro eggs in one basket -  face a difficult time. In a world where money seems to grow on trees, it's hard to take the right measures to prevent Greece from a financial meltdown with unknown consequences.

Questions
Even for actuaries it's hard to understand what's happening and what makes sense or not, It's over our 'actuarial' head....

• Should 'Europe donor countries' support Greece fore more than the '110 billion Euro rescue' in 2010?
  
  
• Is Greece’s 10-year bond rate of 15% an adequate risk premium?
  
  
• Will restructuring Greece's debt solve anything, devaluate the Euro,  or pose other  incalculable risks to the overall Euro zone?

Difficult questions that are hard too answer....


Debt-Deficit Comparison
Let's take an actuarial look at the facts by comparing 2010 Government Debt with Deficit (all in % GDP):

From this chart it's clear that not only Greece is in the danger zone, but also Ireland and the US as well... Moreover, the UK is not free from worries, to put it mildly...

The blind are leading...
Another chart-conclusion might be that the blind are leading the blind'. Relative strong less-weaker countries like Germany and France,  have to carry the financial consequences of cheating and not-performing countries. Above all, we all know: one rotten apple spoils the barrel!!

In fact to save or revive 'Financial Europe' it would take some countries with no debt and a strong positive surplus (= negative deficit) instead of a deficit.

It seems neither sensible nor logical  to restructure another  country's debt if the outlook of the governments debt and deficit of the' helping country' is (slightly less) negative as well. But as we know: only fools rush in where angels fear to tread.

Trying to help other countries that fail to restructure themselves is like banging your head against a brick wall...  No risk premium on government bonds can compensate that...

Countries with a strong relative debt and a deficit should restructure their own country and financial situation at once, before asking ore receiving any outside help.

Growth: The Solution?
Some argue that debt and deficits are not so bad as long as countries are growing. Let's dive into this argument with the next chart (data source: Eurostat):

Indeed, from this 'Growth-Believe' we can now understand why (only) Greece is seen as such a major problem.

From this chart it's also clear that if Ireland and Spain are not going to grow one way or the other, they will become the next big problem. These countries have to take the bull by its horns, before it's too late.

It's throwing caution to the wind when 'debt and deficit countries' with a positive 'Real GDP Growth Rate' try to save sicker country-brothers by lending them money.

Moreover, it's lending money you don't really possess or own, it's like robbing Peter (yourself) to pay Paul....

Combining the two Eurostat charts it becomes clear that that not all 'Garlic Countries' (Mediterranean countries:Greece, Spain, Portugal, Italy) can be lumped together.

Greece is indeed the greatest risk , secondly a non-garlic country: Ireland...
Spain, Portugal and Italy are relatively at arm’s length and could perhaps keep their head above water if they take the right measures in time.

U.S.' Fiscal Gap

Finally, don't forget about the U.S., as the U.S. Real GDP Growth Rate is already declining to 2.3% in Q1 2011.

According to Boston University economist Kotlikoff, the U.S. is broke.  Kotlikoff doesn’t trust government accounting. He uses “Fiscal Gap,” not the accumulation of deficits, to define public debt. This "Fiscal Gap" is the difference between a government’s projected revenue  and its projected spending .

By this measure, the U.S. government debt is $200-trillion – 840 percent of current GDP. 

Conclusions

From all this it's clear Europe is stuck between a rock and a hard place...
Although ECB President Mr. Trichet thinks different, it looks like €-Europe has to choose between two blind goats (Irish saying):

(1) A complete Financial Europe Meltdown in case of endless financing default countries like Greece or

(2) Letting individual default countries go bankrupt, with unsure (systemic) consequences for local banks and other financial institutions that financed or invested in default countries.

How to decide? Guideline:  Of two evils, always choose the less....
As option (1) is clearly putting the cart before the horse, and surely leads to a meltdown, only option 2 is left: QUIT!

Sources and related links:
- Spreadsheet: Used Data, Tables for this blog (xls)
- US Real GDP Growth Rate
- Government Debt and Optimal Monetary and Fiscal Policy (2010)
- English proverbs and sayings (!)
- English deficit (including time table)
- Shadowstats (for the real stats!)
- The U.S. is broke?
- Eurostat: Euro area government deficit at 6.0% GDP (2011) 
- BILD: Interview with Jean-Claude Trichet, President ECB, 15 January 2011

Oversized Supervision?

In April 2011 EIOPA  published  the findings of its 2010 survey:

 REPORT ON REPORTING REQUIREMENTS TO SUPERVISORY AUTHORITIES

applicable to the Institutions for Occupational Retirement Provision (IORPs) in the context of the IORP Directive.

The report analyses several interesting differences in reporting among member states.

I'll will confine myself in this blog to two remarkable results....
 
1. Difference in number of Supervision employees per country
It's remarkable (and not directly explainable) to see that the UK and The Netherlands outnumber the other European countries on number of supervision employees....

 
2. Influence Actuarial Reporting
The survey provides a large number of reporting and monitoring issues that aim to monitor or mitigate several types of risk.
I'll provide a short report that shows the connection between some actuarial reports and types of risk.

Clearly the risk of funding is one of the most important issues with regard to actuarial reporting. Perhaps it's even a little bit overweighted......

Anyhow, check your reports with regard to the above risks, especially if your living in an oversized supervision country like the UK or The Netherlands....

Homo Actuarius Bayesianis

Bayesian fallacies are often the most trickiest.....

A classical example of a Bayesian fallacy is the so called "Prosecutor's fallacy" in case of DNA testing...

Multiple DNA testing (Source: Wikipedia)

A crime-scene DNA sample is compared against a database of 20,000 men.

A match is found, the corresponding man is accused and at his trial, it is testified that the probability that two DNA profiles match by chance is only 1 in 10,000.


Sounds logical, doesn't it?
Yes... 'Sounds'... As this does not mean the probability that the suspect is innocent is also 1 in 10,000. Since 20,000 men were tested, there were 20,000 opportunities to find a match by chance.

Even if none of the men in the database left the crime-scene DNA, a match by chance to an innocent is more likely than not. The chance of getting at least one match among the records is in this case:



So, this evidence alone is an uncompelling data dredging result. If the culprit was in the database then he and one or more other men would probably be matched; in either case, it would be a fallacy to ignore the number of records searched when weighing the evidence. "Cold hits" like this on DNA data-banks are now understood to require careful presentation as trial evidence.

In a similar (Dutch) case, an innocent nurse (Lucia de Berk) was at first wrongly accused (and convicted!) of murdering several of her patients.

Other Bayesian fallacies
Bayesian fallacies can come close to the actuarial profession and even be humorous, as the next two examples show:

1. Pension Fund Management
   
   
   It turns out that from all pension board members that were involved in a pension fund deficit, only 25% invested more than half in stocks.
   
   Therefore 75% of the pension fund board members with a pension fund deficit invested 50% or less in stocks.
   
   From this we may conclude that pension fund board members should have done en do better by investing more in stocks....
   
   
2. The Drunken Driver
   It turns out that of from all drivers involved in car crashes 41% were drunk and 59% sober.
   
   Therefore to limit the probability of a car crash it's better to drink...

It's often not easy to recognize the 'Bayesian Monster' in your models. If you doubt, always set up a 2 by 2 contingency table to check the conclusions....

Homo Actuarius
Let's  dive into the historical development of Asset Liability Management (ALM) to illustrate the different stages we as actuaries went through to finally cope with Bayesian stats. We do this by going (far) back to prehistoric actuarial times.
 

As we all know, the word actuary originated from the Latin word actuarius (the person who occupied this position kept the minutes at the sessions of the Senate in the Ancient Rome). This explains part of the name-giving of our species.

Going back further in time we recognize the following species of actuaries..

1. Homo Actuarius Apriorius
   This actuarial creature (we could hardly call him an actuary) establishes the probability of an hypothesis, no matter what data tell.
   
   ALM example: H₀: E(return)=4.0%. Contributions, liabilities and investments are all calculated at 4%. What the data tell is uninteresting.
   
   
2. Homo Actuarius Pragmaticus
   The more developed 'Homo Actuarius Pragamiticus' demonstrates he's only interested in the (results of the) data.
   ALM example: In my experiments I found x=4.0%, full stop.
   Therefore, let's calculate with this 4.0%.
   
   
3. Homo Actuarius Frequentistus
   In this stage, the 'Homo Actuarius Frequentistus' measures the probability of the data given a certain hypothesis.
   
   ALM example: If H₀: E(return)=4.0%, then the probability to get an observed value more different from the one I observed is given by an opportune expression. Don't ask myself if my observed value is near the true one, I can only tell you that if my observed value(s) is the true one, then the probability of observing data more extreme than mine is given by an opportune expression.
   In this stage the so called Monte Carlo Methods was developed...
   
   
4. Homo Actuarius Contemplatus
   The Homo Actuarius Contemplatus measures the probability of the data and of the hypothesis.
   
   ALM example:You decide to take over the (divided!) yearly advice of the 'Parameters Committee' to base your ALM on the maximum expected value for the return on fixed-income securities, which is at that moment  4.0%. Every year you measure the (deviation) of the real data as well and start contemplating on how the two might match...... (btw: they don't!)
   
   
5. Homo Actuarius Bayesianis
   The Homo Actuarius Bayesianis measures the probability of the hypothesis, given the data.  Was the  Frequentistus'  approach about 'modeling mechanisms' in the world, the Bayesian interpretations are more about 'modeling rational reasoning'.
   
   ALM example: Given the data of a certain period we test wetter the value of H₀: E(return)=4.0% is true : near 4.0% with a P% (P=99?) confidence level.

Knowledge: All probabilities are conditional
Knowledge is a strange  phenomenon...

When I was born I knew nothing about everything. When I grew up learned something about some thing. Now I've grown old I know everything about nothing. Joshua Maggid

The moment we become aware that ALL probabilities - even quantum probabilities - are in fact hidden conditional Bayesian probabilities, we (as actuaries) get enlightened (if you don't : don't worry, just fake it and read on)!

Simple Proof: P(A)=P(A|S), where S is the set of all possible outcomes.

From this moment on your probabilistic life will change.

To demonstrate this, examine the next simple example.

Tossing a coin

• When tossing a coin, we all know: P (heads)=0.5
• However, we implicitly assumed a 'fair coin', didn't we?
• So what we in fact stated was: P (heads|fair)=0.5
• 
  Now a small problem appears on the horizon: We all know a fair coin is hypothetical, it doesn't really exist in a real world as every 'real coin' has some physical properties and/or environmental circumstances that makes it more or less biased.
• We can not but conclude that the expression
  'P (heads|fair)=0.5'  is theoretical true, but has unfortunately no practical value.
• The only way out is to define fairness in a practical way is by stating something like:  0.4999≥P(heads|fair)≤0.5001
• Conclusion: Defining one point estimates in practice is practically  useless, always define estimate intervals (based on confidence levels).

From this beginners  example, let's move on to something more actuarial:

Estimating Interest Rates: A Multi Economic Approach

• Suppose you base your (ALM) Bond Returns (R) upon:
  μ= E(R)=4% and σ=2%
  
  
• Regardless what kind of brilliant interest- generating model (Monte Carlo or whatever) you developed, chances are your model is based upon several implicit assumptions like inflation or unemployment.
  
  The actual Return (R_t) on time (t) depends on many (correlated, mostly exogenous) variables like Inflation (I), Unemployment (U), GDP growth(G), Country (C) and last but not least  (R_[t-x]).
  
  A well defined Asset Liability Model should therefore define (R_t) more on basis of a 'Multi Economic Approach'  (MEA) in a form that looks more or less something like: R_t = F(I,U,G,σ,R_[t-1],R_[t-2],etc.)
  
  
• In discussing with the board which economic future scenarios will be most likely and can be used as strategic scenarios, we (actuaries) will be better able to advice with the help of MEA. This approach, based on new technical economic models and intensive discussions with the board, will guarantee  more realistic output and better underpinned decision taking.

Sources and related links:
I. Stats....
- Make your own car crash query
- Alcohol-Impaired Driving Fatalities (National Statistics)
- D r u n k D r i v i n g Fatalities in America (2009)
- Drunk Driving Facts (2006)

II. Humor, Cartoons, Inspiration...
- Jesse van Muylwijck Cartoons (The Judge)
- PHDCOMICS
- Interference : Evolution inspired by Mike West

III. Bayesian Math....
- New Conceptual Approach of the Interpretation of Clinical Tests (2004)
- The Bayesian logic of frequency-based conjunction fallacies (pdf,2011)
- The Bayesian Fallacy: Distinguishing Four Kinds of Beliefs (2008)
- Resource Material for Promoting the Bayesian View of Everything
- A Constructivist View of the Statistical Quantification of Evidence
- Conditional Probability and Conditional Expectation
- Getting fair results from a biased coin
- INTRODUCTION TO MATHEMATICAL FINANCE