Showing posts with label actuary. Show all posts
Showing posts with label actuary. Show all posts

Jun 13, 2015

Professional Empathy of an Actuary

The most important hard skill in any profession is a soft skill called Empathy.
Without empathy, any project or business goal is doomed to fail.

Just a small humorous illustration to get the picture....

Adding Rabbits

One day, the math teacher asks six year old Johnny, "If you have 200 rabbits and you add another 100 rabbits, how many rabbits would you have?"

Johnny thinks for a moment and then answers: "I think the answer is 337 Sir".

"No, Johnny. That's the wrong answer. Try again.

Johnny takes another five seconds and answers: "Still probably 337 Sir" "

Now the math teacher slightly loses his temper and baffles: "No Johnny, wrong again. You know nothing about mathematics!".

Immediately Johnny answers: "And you know nothing about rabbits Sir"

Actuarial Skills
A recent (May 2015) Investopedia article sums up the five skills every actuary needs
  1. Analytical Problem Solving Skills 
  2. Math and Numeracy Skills 
  3. Computer Skills 
  4. Knowledge of Business and Finance 
  5. Communication and Interpersonal Skills
Although in a classical sense this powerful summary of an actuary's professional competences is perfectly in line wit more detailed descriptions as given by several actuarial associations, something essential is missing.

To be successful as an an actuary you'll need to develop what is called

Professional Empathy


What is Professional Empathy?
Professional empathy is the ability to see the world through the eyes of other professionals.

As actuaries we're trained to view and resolve our projects, challenges and issues in a primarily quantitative manner with the help of actuarial techniques, models and formulas.

Through study and experience we can develop "Professional Empathy" that allows us to look through the eyes of other professionals or clients and feel and understand what their view and perceptions are.
In doing so, we're able to optimize our advice, support or project performance.

Understanding and response
Professional empathy implies two elements: understanding and response.

Understanding
To understand other professionals and clients, we need to develop the ability to be sensitive to the needs and feelings of others. To do so, it helps to develop technical skills in other professional fields, read other than just actuarial literature and join other than pure actuarial conferences.

As graduated actuaries we already developed a broad multi-professional basis that includes professional areas as: mathematics, statistics, finance, insurance, asset management, pricing, administration, business analytics, ict, organization, marketing, etc. Therefore, if we continue to develop this multi-professional ability, we - as actuaries - are are ideally suited to organize combined professional expertise (innovation) projects.

Besides the traditional actuarial areas as insurance, pensions, statistics, and actuarial techniques we'll have to focus and develop skills in the surrounding areas of the actuarial work field:



In order to understand we need to develop the ability to read verbal, paralinguistic and non verbal cues of professionals in other professional fields.

Response
Secondly, our response and attitude to other professional should be in such a way that other professionals recognize that we understand them, appreciate that we speak their (professional) language and invite us to share their professional issues or doubts with us.

Interpersonal communication skills 
This way of responding requires to bring out a professional attitude that's based on interpersonal communication skills like:
  1. showing interest, respect and appreciation
  2. active listening
  3. showing understanding, being accessible, 
  4. having a flexible attitude 
  5. operate steady on basis of ethical principals.

Intrapersonal skills
It also forces us actuaries to continuously work on our intrapersonal skills like: 
  1. Knowing what drives, angers, motivates, frustrates, inspires you
  2. Knowing your own strengths and limitations
  3. The ability to stay calm and balanced in stressful situations
  4. Self confidence

How to start with Professional Empathy?
You can start with developing your professional Empathy as follows:
  1. simply pick out one of the communication issues as mentioned above (e.g. 'active listening')
  2. After a conversation with a trusted professional you work with, simply ask for honest feedback by asking for example: "I try to develop a more active listening style. May I ask you: Do you think I really listened to your arguments. If so, in what way? If not, 'why' and 'when' not? What would you have expected of me?

If it seems difficult for you to ask these questions, congratulations! You now know for sure this approach is applicable to you.

If you think it makes no sense to ask these questions once in a while. Just keep doing what you always did until the final reality check!

SOA Self Assessment
The Society of Actuaries has developed a Competency Framework Self-Assessment Tool. The self-assessment asks you to rate a series of statements about the skills that actuaries should have to be valued for their professionalism, technical expertise and business acumen.

It's a 45 minute test that gives first impression of you improvement areas. However, interpersonal en intrapersonal skills are only limited measured.........

Sep 26, 2013

Actuarial Cookery in the Boardroom

Suppose your friend gave you the recipe for a delicious 'Paleo Tomato Soup'.

Does that recipe also guarantees you a delicious meal ?

Undoubtedly you answered this question with a clear "no".

Why?

As we all know, it is the 'touch of the chef' that determines the quality and final taste of the meal. The recipe is the score and the chef the performer of the culinary piece of music, that will end up on your plate.

Although the above example probably sounds logical to us, the actuarial cooking practice appears different. Let's take a look at the next example.

An Excellent ALM Advice
What about a 'plate of five' asset mix advice that's on the board's breakfast table, as the ultimate outcome of your excellent ALM analysis...

Does this ' computer recipe' actually guarantees a sound decision about an adequate investment policy?

Actually, the answer to this question can hardly be other than 'NO'.

Your advice is a static advice in a dynamic world and - on top of - the final question remains whether the asset manager is able to 'spice up' your recipe.

The actuary as Risk-Director
Key question is whether we as a profession - keeping ourselves inadvertent in the role of  'technical experts' - merely feel responsible for delivering the recipe for a cold asset mix salad on basis of 'expected values' ​​and variances.

Or ... that we actuaries are willing to act as 'risk-director' in the interactive process of creating a dynamic investment policy that's based on a nonlinear constructed healthy and varied based asset mix over time. Albeit..., without taking the driver's seat in the advice process, but with the obligation to report the eventual existence of any GMCs ('Genetically-Modified Cickens') in the asset-mix.

Economic Risk Management or ALM?
In the thorough process of adopting a dynamic investment policy, financial boards more and more take decisions based on the study of different future economic scenarios.

This development challenges actuaries to invest more in the development of "Economic Risk Management" (ECRM) models instead of traditional ALM modeling. In ECRM 'asset class data' (as in ALM) and economic data (GDP, inflation, consumer confidence, etc) are mixed in an integral set of data, that's analysed and - with future expectations, 'stress-test conditions' or of 'believes' -  (nonlinear) translated and optimized in a dynamic asset mix.

This economic risk approach requires new nonlinear economic-asset models that urge for a close cooperation between economists and actuaries, resulting in an serious interactive board discussion (board members and economical & actuarial experts) of the ECRM models.

This approach is not limited to the well-known three or four so-called 'muddle through scenarios', but covers the outcome and impact of a large number of more precise formulated possible economic scenarios on the asset mix and the investment strategy.

Scenarios that help determine the overall risk appetite and result in a major impact on the composition of the strategic asset mix.

New Q&A's
In other words, new scenarios that give answers to questions like:

As with the current ALM approach, the focus should not be only on the quantitative outcome of the ECRM model, but more on the discussion and wider perception of how economic risk affects the optimal asset mix and the dynamic asset policy, allowing boards to take more informed and underpinned investment policy decisions.

In this approach, ALM and ECRM are helpful but not dominating decision support tools in the creation of the final investment policy and not an unintended consultant's dictate that's implicitly adopted ("take note") by the board and then subsequently implemented.

How to Check the Quality of your ALM or ECRM Advice?
Fortunately, it is easy to check whether your ECRM or ALM advice is actually a good quality decision document or just a bite-sized chunk.

If your advice offered only 'one option' or was adopted without a serious debate or any amendments, then -  to put it euphemistically - your advice is 'ready for improvement'.

Actuaries: Backroom to the Boardroom
Finally, it all comes down together whether we as actuaries want to profile ourselves as 'recipe writers' or pick up the 'risk-director role' as an 'actuarial chef'. If you choose the latter, please stand up and help to bring out actuaries from the Backroom to the Boardroom. Success!

Jun 13, 2011

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

May 25, 2011

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

May 10, 2011

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 (Rt) 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 (Rt) more on basis of a 'Multi Economic Approach'  (MEA) in a form that looks more or less something like: Rt = 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

Apr 18, 2011

Actuary beats Chimp (or not?)

Is your joy for stats just as big as Hans Rosling's enthusiasm shows?

Just watch how Hans tells the developing story of Lifespan against Income in more than 200 years, showing around 200 countries in a 120.000 numbers flowing chart!



It's clear, we actuaries can learn from Hans with his (1) 'sticky enthusiasm' and (2) 'keeping the issue on headlines'.....


You can play for your own with the data Hans uses on Gapminder World



No doubt..., your next board presentation will be dynamic and flowing.

To conclude... If you as an actuary think you know more about the actuarial world than a chimp, please take Hans Rosling's



on facebook.


Let's hope for the 'best'.....

Related links:
- Gapminder World

Mar 27, 2011

Zero Problems Risk Management

More and more we actuaries and risk managers become aware that our risk models can't just be based on numbers and statistics exclusively.

Some examples:

Systemic risk
The recent financial crisis made it clear that a 'mono risk approach' on a sole risk-object (mortgage, fund-investment) is insufficient.

Investments and loans are embedded in a worldwide sea of connected financial instruments and reinvestments. Systemic risk has to be included in our models.

Main challenge here is that systematic risk essentially depends on macroeconomic and (mostly) irrational factors. Further, systemic risk is related to the structure and dynamics of the market. More than numbers.....

Supervisory Herding Risk
In their effort to control and support financial institutions like banks, pension funds and insurance companies, country supervisors, regulators and 'accounting standards boards', defined a meticulously set of guidance rules (Basel I/II/II, Solvency I/II, Qis-I-V, IFRS, FAS, AIFMD, FTK, FIRM, etc.,etc.)

Financial institutions not only confirmed and adopted to those new rules, but - in their rush and driven by cost and time pressure - also implicitly (and often unintentionally) declared those same imposed rules and rationales as their own business 'Risk Appetite'. This way, most financial Institutions became so called: 'Supervisory Compliant'.

Instead of  expliciting their specific company-targets and successively developing their own correspondent risk appetite and risk framework, they incorporated the supervisor's risk philosophy. 

Without a sound own (board) risk vision that would undoubtedly have included some extra safety on 'company specific risk issues', financial institutions became - like a herd - all in the same way extremely vulnerable to (less defined) external risks.

Summarized:

Overregulation increases Herding Risk

Financial institutions all measure and respond to regulated risks in the same way. Supervisory Herding Risk is born.....

Too Much Focus Risk
As a consequence of pre-subscribed risk categories and ruling by law or (accounting) standards, there's the risk of 'too much focus' on specific risks while forgetting, denying or neglecting other important risks. Remember, the devil is in the (correlating) details....

Here's a useful, but not exhaustive, checklist to keep track on your risk models...

Average Premium Risk Diversification Risk Matching Risk
Commodity risk Employer Continuity Risk Operational risk
Compliance Risk Environmental Risk Outsourcing Risk
Compliance Risk Equity Risk Oversight Risk
Concentration Risk Herding Risk Price Inflation Risk
Counterparty Risk Interest rate risk Property Derivatives Risk
Coverage Ratio Risk IT Risk Reinsurance Risk
Credit Risk Legal risk Reinvestment risk
Culture Risk Legislative Risk Reputation risk
Currency Risk Liability Risk Sex Calculation risk
Default Risk Liquidity risk Strategic risk
Deflation risk Longevity Risk System Risk
Disaster Risk Market Risk Systemic Risk
Discount Risk Matching Risk Wage Cost Inflation Risk

ALM Simplifying Risk
Univariate models are killing and even multivariate models have proven to be too vulnerable and too limited in the recent crisis. It's not just about correlation and covariance matrices. What we need is an self-explaining model. A model  that predicts or generates expected values in an economic context, depending on exogenous economic variables like inflation rate, GDP-Level, etc. and that is based on the same structured historical economic data-set.

We need 'Asset Liability Modeling New Style' and not only Stress Testing or
advanced and excellent Crash Modelling as well explained by EMB.

Geopolitical Risk
With Europe and Japan as recent examples, it's clear that risks come from everywhere around the world.

The consequence of earthquakes (Japan, Australia), a possible  country default (Ireland, Greece, Portugal, ..), political instability (Libya, Ivory Coast, ..), war threat (Vietnam,Iraq, ..), financial easing (US, Europe,...), on our economic system, prices and financial institutions seems substantial and - moreover- predictable.

More than just trying to catch and capitalize these kind of risks in our risk models, we need to develop (financial) mechanisms and products that can cope as best as possible with these kind of risks.

The Riskmap 2011, Managing Risk | Maximising Opportunity, offers a good description of the actual risks that influence our lives and risk models.

A nice example is the recent (unexpected) leading role that France took in action against Libya.  'Riskmap 2011' mentions the 'Arabic Poll 2010' that clearly shows (despite the lack of sympathy for president Sarkozy) the trust and sympathy for France. France clearly outperforms the US and president Obama  unfortunately has lost the trust of the Arabic world... Take a look at the next slide summary (or the original complete pdf):  

Arab Public Opinion Poll 2010 Summary

The Arabic poll shows that the prime minister of Turkey, Erdogan, has clearly gained  the confidence and trust of the  Arabic countries. With Ergodan, Turkey - at the cross road between East and West - takes a leading role in the 'World Risk Management Process'.  Ergodan's Risk Philosophy, invented  by the Turkish Foreign Minister Ahmet Davutoglu,   is 'Zero Problems'.....

Perhaps that should be the philosophy of actuaries too...

Zero Problems


Conclusion
From now on 'Modeling Risk' is more than just a financial exercise.
It's building scenario's, mechanisms and products that can cope with this risky world.  Success as actuary or risk manager!

Related Links:

- Committee (behaviour) assessment tool
Control Risks:Riskmap 2011
- Arabic Poll 2010
- Supervisory Compliant
- Maplecroft Risk Maps 
- EMB: How to Model a Crash (REVO) 

Mar 11, 2011

Groupthink

IMF evaluated its role and performance in the recent financial and economic crisis.

Cause
In a 2011 crisis report with the short title: 'IMF Performance in the Run-Up to the Financial and Economic Crisis:IMF Surveillance in 2004–07 ', IMF concludes that the main cause of their inadequate response during the crisis was:



Groupthink


IMF’s ability to detect important vulnerabilities and risks and alert the membership, was undermined by a complex interaction of factors, many of which had been flagged before but had not been fully addressed.

The IMF’s ability to correctly identify the escalating risks was hindered by:
  1. A high degree of groupthink 
  2. Intellectual capture
  3. A general mindset that a major financial crisis was unlikely
  4. Inadequate analytical approaches
  5. Weak internal governance
  6. Lack of incentives to work across units and raise contrarian views
  7. A review process that did not “connect the dots” or ensure follow-up
  8. Some impact of 'political constraints'....


Recommendations
IMF suggests some recommendations on how to strengthen its ability to discern risks and vulnerabilities and to warn in the future. Main point is to enhance the effectiveness of surveillance: it is critical to clarify the roles and responsibilities of the Board, Management, and senior staff, and to establish a clear accountability framework.

Looking forward, IMF needs to
  1. Create an environment that encourages candor and considers dissenting views
     
  2. Modify incentives to “speak truth to power”
     
  3. Better integrate macroeconomic and financial sector issues

  4. Overcome the silo mentality and insular culture; Deliver a clear, consistent message on the global outlook and risks.

Recognize Groupthink
Groupthink is not just something happening to IMF or 'other organisations'. We, financial institutions, all suffer somehow or somewhat from the Groupthink Virus.

How can we recognize Groupthink?
Derived from an article by Irving Janis, the inventor of the word Groupthink, let's take a look at some explicit signs of Groupthink:

  1. Winning Mood syndrome
    A common illusion of success (Folie à deux), invulnerability, over-optimism, unanimity and risk-taking as a consequence.
  2. Collective rationalization
    Managers, employees discount warnings and do not reconsider their assumptions
  3. Repression or Ridicule
    Direct pressure on and ridicule of  individuals who express disagreement with or doubt about the majority view or the view of the leader
  4. Fear
    Fear of disapproval for deviating from the group consensus. Fear from or doubt about expressing your opinion.
  5. Manipulating
    Remaining silent in a discussion is implicitly interpreted as agreeing.Obviously 'wrong' arguments are used to achieve a certain goal or policy.
  6. Disrespect
    Stereotyped views of out-groups or enemy leaders as evil, weak or stupid. Good or serious ideas of colleagues are rejected on basis of the source instead of 'judged by the facts'.
  7. Moral Blindness
    Unquestioned belief in the inherent morality of the in-group. Lack of discussion about ethical or moral aspects of certain decision.
  8. Miscommunication and Misinformation
    Information, bottom up or top-down is (deliberately) strongly filtered
  9. Idolization
    Idolization of the leader or of certain five star employees.


Lessons Learned
If you recognize some of the above signs in your organization, it is time for action.
Discuss it, do not accept it and if you cannot change it... LEAVE!

A humorous example of Misinformation are the quotes of Iraq's minister of (Mis)Informaton, Al-Sahaf, during the 2003 Iraq war.
Enjoy, laugh and learn.....



Make sure your board presentation is not based on' sahaf-statements' but on simple provable actuarial facts....

Related links/sources:
- 8 signs of groupthink
- What is Groupthink?
- IMF Crisis Report 2011

Feb 6, 2011

Solvency II: Standard or Internal Model?

Solvency II is entering the critical phase.Time is running out!

But...., as a wise proverb states:

"When The Actuaries Get Tough,
The Tough get Actuaries"

However, the market for actuarial resources is limited and Solvency II Actuaries that  combine strategic and technical knowledge with 'common sense' are like  white ravens.

In the case of Solvency II, actuaries and models are moving forward in a particular way.

Standard Model
Originally, the 'standard model' was foreseen as a simple model for small and mid-size insurers (apart from very small insurers that were excluded). Big insurers, with more developed actuarial models, larger scale and more resources, were expected to work out a more sophisticated 'internal model'.

As the Solvency II Time Pressure Cooker gets up steam, things start turning.

Small and mid-size insurers found out that the 'standard model' was highly inefficient and the wrong instrument to steer adequately on risk management and to determine adequate solvency levels in their company.

Just because of their limited size and product selection, small and mid-size insurers often already have a well tuned risk management system in place and implemented throughout the organization. The manager, actuary (being the risk manager as well) and CFO of such companies therefore have enough time to develop a formal Solvency II 'internal model' that could be easily implemented throughout their organization.

Internal Model
Quit the opposite happens in the world of big insurers.

Big insurers coordinated Solvency II at Holding level and started to challenge their business-units around 2009 to develop and implement Solvency II programs on basis of an 'internal model'.

Collecting homework at the Holding in 2010, it became clear that a lot of technical issues in the models were still unclear. Moreover, models were not integrated (= condition)  in the business and counting up several 'internal models' showed up several consolidated inconsistencies. 

The complexity of developing a consistent risk model turned out to strong. Some big insurers are now considering to fall back on the 'standard model' (or partial model) before it's too late: the shortest errors are the best.



Looking back it's not surprising that big insurers need more time to operationalize a fine tuned risk model. It took specialist Munich Re 10 years to implement an internal model.

This development is also an indication that some big insurers are strongly over-sized. In order to keep up with the speed of the market, big insurers have to be split up into a manageable and market-fit size.


Related Links:

- Surviving Solvency II (2010)
- The influence of Solvency II on an insurer’s strategic policy
- White Ravens and Black Swans (Math Fun)

Jan 18, 2011

Actuarial Chess?

As actuaries we often have to explain HOW variables like profits, mortality, investments or costs will develop in the future.

In doing so, it would really help and strengthen our credibility if we were able to explain also WHY these variables developed in the past as they have developed, as a result of certain circumstances (other 'explaining' variables).

On basis of these WHY-arguments and the specific expected future circumstances, we could increase the credibility and diminish the volatility of our predictions.

This HOW-WHY-Insight urges us for example to analyze "medical developments" in case of predicting longevity and to study "economic developments" with regard tot predicting future costs, inflation or investment rates.

Moreover this understanding obliges us to develop our capabilities and competence to explain certain given outcomes like "increasing longevity" and "increasing stock return volatility".

Test Your 'Outcome Explanation Competence'
This 'Outcome Explanation Competence' (OEC) is key in actuarial science. No actuary can do without!

To test your OEC level, solve the next chess problem.

Black has made the last move... Which move?



You'll find the solution of this chess problem as a part of the next 5 minute 'Thinking Out of the Box' test ( on SCRIBD)......

5 Minute 'Thinking Out of the Box' Test

Just like in 'climate change predictions', our OEC (the 'competence to explain past phenomena') is necessary for us actuaries to be confident about our theories and predictions about the future.

However, developing OEC might not be enough as the explanations of the past could turn out to be fundamentally invalid with regard to the future. New techniques  like High-frequency trading (HFT) might come up. Or... in chess vocabulary: 'A pawn may promote to a Bishop' (frequency: 0.2%)

The conclusion must be that Actuarial predictions are a kind of 'Actuarial Chess':
So start practicing as an Actuarial Chess Master by Explaining the past and Guiding the future.

Dec 31, 2010

2011: Happy Risk Year!

Life is full of Risk..  We can not deny or totally exclude risk. Have you ever thought about living a (professional) life without taking any risk? What kind of life would that be?

There's this great actuarial risk quote of the famous economist John Maynard Keynes that states:

On the long run, we are all dead.....

So if you want some 'return' in life, you might as well take 'somewhat' risk before you 'certainly' die.

A nice illustration of total risk aversion is the 2004 movie "Along came Polly" were Reuben Feffer (actor Ben Stiller) is an actuary who, since his job involves analyzing risk for insurance purposes, likes living life in complete safety and free from any unnecessary risk.

This movie urges to ask yourself a simple question:

What's the risk of a riskless life?

Living life without risk if for dummies! Optimize the risk-return in your life.

Risk Guidelines
At the end of 2010 some simple Maggid 'Risk Guidelines' for 2011:
  1. As long as there are no risks that'll kill you on the 'middle' or 'short' term: Take risk if you like the return outlook.

  2. Think about how much bad luck or suffering you're willing to accept for a desired return.  Key question here is:
    Why does a marathon runner punish his body every day for weeks on end for an individual race?

  3. Take a small risk every day! Invest small 'good things to do' by helping others without expecting a return. Soon you'll harvest some of your sowed investment seeds.....


Riskless Investment
Remember..., the only one riskless investment in life is.....



YOU




Anyhow, make 2011 a happy and healthy risk year!

Related Links:
- "Watch the movie 'Along came Polly' online !
- Learning about Risk and Return: A Simple Model of Bubbles and Crashes