Risk Management in Demand

As the global financial disaster continues unabated, research is beginning to percolate findings about some of the causes of the storm, as well as the precautionary measures that might avert future crises of this nature. In a recent survey of over 500 key financial executives conducted by MPI Europe (April 2009), several important views prevailed. One of the survey's strongest findings was the perceived need to develop a “risk management culture” in today’s financial institutions, including bolstering the relative power of risk management functions vis-à-vis its trading counterparts. Now, as good as that sounds, I am skeptical as to whether our financial services industry has it within itself to embed a new risk-aware culture without demonstrable intermediate measures to lead the way (after all, our world is inspired by capitalism). The good news is that several other findings were more specific and actionable. Over 75 percent of the respondents saw a shortage of sufficiently and appropriately trained personnel as having a “high impact” on creating the crisis. Additionally, a significant majority of respondents wanted to see an improvement in their “risk management applications,” to include a shift from predominantly quantitative measures toward qualitative methodologies (e.g., internal controls). Both of these latter measures are fully actionable through increased investment in risk management technologies and training. Moreover, implementing stronger spreadsheet control regimes, coupled with stricter guidelines for spreadsheet checking and auditing, are another immediate requirement. Finally, I would argue that by funding and initiating improved risk management technologies and training, executives will be taking the first vital steps toward creating the risk management culture that they seek. The recognized need for effective risk management is gaining traction in today’s financial services industry. The real question remains whether the industry’s leaders will have the courage to recognize the deficiencies of their existing risk management structures, and respond by investing in the technologies and training that can address these shortcomings.

Enterprise Risk Management is a Farce

In the wake of the still expanding ecological crisis in the Gulf of Mexico, management experts are beginning to ask critical questions about the nature and effectiveness of enterprise risk management. Sam Friedman of National Underwriter Property and Casualty reaches this conclusion:

The BP oil spill disaster exposed serious shortcomings in both technology and regulation, but the biggest culprit is a catastrophic failure of enterprise risk management.

Most Americans presume that a multi-billion dollar energy firm of BP’s stature has the resources, technology, and skills to recover oil in a manner that is both profitable and safe for society. However, the extent of the ongoing catastrophe defies this presumption. According to Friedman:

It's hard to imagine a scenario any worse than this. An offshore oil rig explodes, killing 11 workers. The rig collapses. Oil keeps gushing from a deep-sea well, threatening the Gulf Coast, Florida Keys and perhaps even the Eastern Seaboard.

Of course, even multi-billion dollar global corporations can make mistakes, and so society takes great comfort in knowing that the Federal government is diligently regulating and inspecting high-risk industries in order to protect its citizens from the deleterious effects of ecological devastation. Once again, the public apparently presumes too much.

The BP oil spill is not only a stain upon enterprise risk management as an occupation, but upon the entire US regulatory establishment as well. Let’s face it, enterprise risk management in America is a farce.

Source: Friedman, S (2010, May 31), BP Oil Spill a Stain on Risk Management, National Underwriter Property & Casualty.

The Purpose of Risk Management

Here is a useful quote from Prof Emanuel Derman about the nature and purpose of risk management:

When I worked in firmwide risk I didn't clearly understand what our job was -- control risk, measure risk, whatever? Recently I heard a very good logical and philosophical talk by Steve Allen of NYU on the whole business. He's the author of Financial Risk Management: A Practitioner's Guide to Managing Market and Credit Risk. One of the things he said is that the purpose of risk management is neither to control nor measure risk, but rather to make risk transparent. It's such a clear and sensible idea. And, he said, and I think I'm quoting him correctly, that a risk manager shouldn't get fired if a firm loses a lot of money. They ought to get fired if a loss occurs owing to some risk that he didn't bring to the firm's attention.

Source: Derman, E (2007, March 13), The Purpose of Risk Management, Emanuel Derman’s Blog on Wilmott.

Practical Steps for Corporate Risk Management

The Economic Intelligence Unit (2009, "Managing Risk in Perilous Times: Practical Steps to Accelerate Recovery," London) recently published a white paper admonishing corporations regarding the practical steps necessary to accelerate recovery during these perilous times. The lessons and steps outlined in the report are instructive for corporations of all industries:

1. Risk management must be given greater authority.

2. Senior executives must lead risk management from the top.

3. Institutions need to review the level of risk expertise in their organization, particularly at the highest levels.

4. Institutions should pay more attention to the data that populates risk models, and must combine this output with human judgment.

5. Stress testing and scenario planning can arm executives with an appropriate response to events.

6. Incentive systems must be constructed so that they reward long-term stability, not short-term profit.

7. Risk factors should be consolidated across all the institution’s operations.

8. Institutions should ensure that they do not rely too heavily on data from external providers.

9. A careful balance must be struck between the centralization and decentralization of risk.

10. Risk management systems should be adaptive rather than static.

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Elevating the Roles of Risk Analysts and Managers

The ongoing economic crisis has brought with it a heightened interest in risk analysis and management. Yet, something seems awry at the risk management desks of our nation’s largest banks and financial institutions. Goldman Sachs CEO, Lloyd C Blankfein was recently quoted as saying, “too many financial institutions and investors simply outsourced their risk management -- rather than undertake their own analysis, they relied on the rating agencies to do the essential work of risk analysis for them.” Mr Blankfein contended that banks and financial institutions must elevate the status of risk analysis in order to alleviate the “systemic lack of skepticism” that he alleged was precursor to the ongoing economic crisis. Mr Blankfein suggested that banks and financial institutions redefine the roles of risk managers, including giving them equal stature “with their counterparts in revenue producing divisions.” This redefinition of roles includes delegating greater responsibilities and authority onto risk managers, whereby “if there is a question about a mark or a disagreement about a risk limit, the risk manager's view should prevail” (WSJ, April 7, 2009). My personal interpretation of Mr Blankfein’s statements is that those trained and charged with risk analysis and management were apparently left standing outside the boardroom as our nation’s banking and financial “executives” undertook investment policy-making in isolation. Let us hope that the regulatory changes to come might at least mandate that risk managers and analysts get a seat at the table.

The Risk Paradox

Risk management is too often perceived to be a perfunctory activity assigned to the lower echelons of the firm. As described by Douglas W Hubbard (2009, p. 174):

The paradox of risk management... is that the most sophisticated risk analysis methods used in an organization are often applied to low-level operational risks, whereas the biggest risks use softer methods or none at all.

How do we elevate the stature of risk management (and analysis in general) in the executive suite?

Reference: Hubbard, D W (2009), The Failure of Risk Management: Why It's Broken and How to Fix It, Hoboken, NJ: John Wiley.

Risk Management in Review

I recently responded to a question in a public forum regarding the limitations of risk management. The specific question posed was, “What limits our ability to effectively manage risk?” This is an interesting question given the financial crisis still underway. My response follows:

Risk management continues to be a misunderstood discipline. The truth is we do have the analytics to understand and manage most kinds of risk (at least to some extent). The more serious problem confronting our society is our apparent inability to apply that knowledge. I'm reminded of the story of a civil war soldier who was listening to one of his officers read from a newspaper. The story goes that the officer quoted from a story by commenting, "...it says here there were fifty percent casualties at the battle of..." The soldier who was listening to the officer's comment responded by asking, "...wow, is that a lot?"

The point is that having the analytics to describe risk, and having the knowledge and training to understand the analytics are two different things. My impression is that many (if not most) business leaders are poorly trained at understanding risk analytics beyond what might be described as layman's terms. What is most needed today is for our business leaders to become more knowledgeable of how to understand and use risk analytics in an effective and meaningful manner. The days of making guesses based on institutions are long gone, especially when those decisions can result in losses of billions of dollars, as well as suffering amongst the ranks of employees and other stakeholders who are ultimately victimized by those decisions.

My advice to business leaders at all levels is to make risk analysis a centerpiece of their training in graduate school. If you puzzle over terms such as variance, standard deviation, stochastic, optimization, and so forth, then it may be time to schedule some training in these skills as part of your lifetime learning plan.

The Future of Risk Management and Financial Ratings

I recently came across a thought-provoking table purporting to compare the present and future of risk management and financial ratings (2009, Ontonix). Most of the predictions are illuminating. However, I doubt that financial risk analysts will migrate away from statistics and Monte Carlo simulation (MCS) models toward "model-free" methodologies (whatever that means). In fact, I predict that the use of statistics and MCS methods in financial risk analysis will expand in the coming years. Nevertheless, I find the juxtapostion of the conventional and future to be parallactically interesting and intriguing.

Reference: "The Present and Future of Risk Management and Rating," (2009), Ontonix Complexity Management.

Risk Analysis

I am frequently asked by analysts and executives to recommend a book that provides practical methods and clear guidance for making risk and uncertainty more transparent, evident, understandable, and manageable in support of complex decisions. The book I turn to most often is Risk Analysis by David Vose (2008). Included with the book is a 90-day license for ModelRisk (Vose) software, which I also recommend and use in private practice. According to the publisher:

Risk Analysis concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.... Based entirely on proven techniques, this superb guide is designed for those who do computer modeling. Stressing the accuracy of risk modeling rather than the management of risk analysis, it provides excellent guidelines for modeling uncertainty using spreadsheets and Monte Carlo software. Features sufficient probability and statistics theory for readers to be confident of the correctness of the methods and understand when and how the theories can be used.

I use Risk Analysis with my university students, and also in professional development training that I conduct around the world. The book includes scores of graphs, charts, practical exercises and model solutions. Additionally, the book makes frequent reference to ModelRisk, the most powerful spreadsheet-based risk analysis software currently on the market. The help files included with ModelRisk are worth the cost of the book alone. I recommend Risk Analysis by David Vose to anyone seeking to master or just learn more about the art and science of professional risk analysis.

Source: Vose, D (2008), Risk Analysis (3rd edition), John Wiley & Sons.

The Elephant in the Room of Risk Management

by John Berling Hardy © The Hidden Game Revealed

One of the cornerstones of our accounting, banking, and investing models is the assumption that pervasive collusion is an anomaly – a kind of perfect storm that needs many pieces to be in place for it to be successful. What if this assumption was false? What if pervasive collusion, instead of being an aberration, were something as mundane as rust on metal? It would imply that in the absence of specific safeguards, pervasive collusion would be the stasis towards which organizations and industries would gravitate.

Social systems in general gravitate towards oligopoly. Take any small town with a history that extends over a century and you’ll find a small clique who runs it. Observe any high school, be it in a slum, or in Hollywood, and there’ll be a ‘cool crowd’ that lords over the rest. So why then should we expect corporations to be any different?

Accepting this possibility would have serious implications for our entire approach to risk management. This would radically impact our risk assessment calculation. What was before thought of as known risk did not include this contingency, therefore the way in which investments and lending institutions went about lending their funds seriously underestimated the true risk relate to their investments. It would also imply that we had to rethink our current approach to corporate governance, and internal controls (SOX).

As this type of phenomenon tends to be more acute in larger organizations, it stands to reason that this is the sector in which the banks sustained the greatest losses. Their knee-jerk reaction was to then tighten up credit in the medium and small sized business sector. This compounded the economic impact of their mistakes, while doing nothing to address the real source of the problem- the Player Culture that predominated at the top of so many large organizations.

There is a naturally occurring phenomenon; I refer to as the Hidden Game Algorithm, wherein a small group of Players are able to create a Player Culture within a company, thereby transforming it into their personal proxy. Those megalomaniac CEO’s, such as Jack Walsh at GE, or Kenneth Lay at Enron, were merely the tip of the iceberg, this represents an ethos that has pervaded our entire business culture.

I believe it is high time we removed our rose coloured lenses and began to deal with this “elephant in the room”. It is time we removed these corporate celebrities and their circles of influence from their perches at the top of banking and industry, and replaced them with those who have the intelligence, and intent, to lead us forward.

Reproduced with permission of John Berling Hardy at The Hidden Game Revealed

On Model Risk

I mentioned in my last posting that I am frequently asked to explain in practical terms what is meant and implied by financial modeling and risk analysis. However, the better question would be to ask about the meaning and nature of model risk in financial economics. Here is a pragmatic definition of model risk offered by Dr Riccardo Rebonato (2002):

Model risk is the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market.

Dr Rebonato's "definition" is more of an operationalization, which nevertheless offers useful guidance for analyzing model risk in practice. Financial modeling and risk analysis are legacy concepts; the front of mind practical issue of our time centers on model risk and its implications.

Source: Rebonato, R (2002), "Theory and Practice of Model Risk Management,” Unpublished manuscript.

Excel + ModelRisk: Practical Spreadsheet Risk Modeling for Management

Risk professionals seeking practical methods for modeling risk using spreadsheets will be interested in the new textbook, Practical Spreadsheet Risk Modeling for Management (Lehman, Groenendaal & Nolder, 2011). Click the image below to preview the contents.

[click image to preview]

I have adopted Practical Spreadsheet Risk Modeling for Management as the standard textbook for my online training courses linked below.

Risk Modeling & Decision Analysis via Excel + ModelRisk

Related Posts

In Defense of Financial Theories

I recently read a ridiculous critique of Value at Risk (VaR) by Pablo Triana in BusinessWeek (“The Risk Mirage at Goldman,” Aug 10, 2009). His review of this advanced financial technique is scathing:

VaR-based analysis of any firm's riskiness is useless. VaR lies. Big time. As a predictor of risk, it's an impostor. It should be consigned to the dustbin. Firms should stop reporting it. Analysts and regulators should stop using it.

Mr Triana bases his assertion on the observation that VaR is “a mathematical tool that simply reflects what happened to a portfolio of assets during a certain past period,” and that “the person supplying the data to the model can essentially select any dates.” My response to his argument is simply to ask, “Isn’t that true of any model or theory…?” Mr Triana goes on to argue that:

VaR models also tend to plug in weird assumptions that typically deliver unrealistically low risk numbers: the assumption, for instance, that markets follow a normal probability distribution, thus ruling out extreme events. Or that diversification in the portfolio will offset risk exposure.

In essence, Mr Triana seems to be saying that normally distributed results have bounds, and that portfolio diversification does not offset risk. Neither of his assertions are supported by probability theory or the empirical evidence. Yet, Mr Triana goes on to conclude, “it’s time to give up analytics so that real risk can be revealed.”

Mr Triana does a disservice to the financial services industry and public at large with his dramatic commentary. Yes, the discipline of finance has much to learn from the ongoing economic crisis, and of course, financial theory in general will evolve based on these recent lessons. However, just because one gets a bad meal in one restaurant does not mean that one should quit going to restaurants.

Financial theories such as VaR stand as state-of-the-art tools in the business of finance and risk management. These techniques are grounded in the same stochastic methodologies that are used by engineers in virtually every industry. To dismiss VaR so completely without considering its utility for supporting effective financial decisions is tantamount to sending financial theory back to the dark ages. Our knowledge of finance needs to advance as a result of what is happening in the economy, not go backwards.

A Regulatory Architecture for Cross-Border Banking Groups

by Stefano Micossi © VoxEU.org

Policymakers and commentators have suggested that large banks should be broken up. This column argues that such an idea risks the very existence of a global financial system. It outlines an alternative framework in which deposit insurance should be covered by banks not taxpayers, banks should not be guaranteed a bailout, and regulators should be mandated to step in when the warning signs begin.

Following the demise of Lehman Brothers, the debate on regulatory reform has led to the conclusion that large banking institutions must be broken up and their risk-taking activities limited by law along the lines of the ‘Volcker rule’ (Gros 2010). Not only are such actions unnecessary, they may be hard to implement and could reduce the availability of credit to the economy (if for example they reduce the ability of banks to hedge their credit positions). The main consequence of such a plan would be the disintegration of global financial markets as they break down into segregated national markets.

In recent research, my colleagues and I set out an alternative solution that can achieve a more stable and resilient financial system without renouncing the benefits of global and multi-purpose financial institutions and innovative finance (Carmassi et al 2010). These are predicated on effectively curtailing moral hazard and strengthening market discipline on banks’ shareholders and managers by raising the cost of the banking charter to fully reflect its benefits for the banks, and restoring the possibility that all – or at least most – large banks can fail without unmanageable systemic repercussions.

Back to basics

The crisis was generated by cross-border banks levering their deposit base and acting, through the wholesale interbank market, as the residual suppliers of liquidity for all the other players in financial markets. This multiplied funds for speculation and helped to sustain a gigantic inverted pyramid of securities made up of other securities and yet again other securities. Without the money-multiplying capacity of the banks, the asset price bubble and the explosion of financial intermediation and aggregate leverage would not have been possible. Extreme investment strategies by bankers obviously reflect moral hazard created by the expectation that governments would step in to bail them out in case of large losses.

The first thing that needs to be done in order to restore corrective incentives for bank managers and shareholders is to eliminate the most obvious pitfall in banking regulation, that is, reliance on capital requirements based on risk-weighted assets. This approach is flawed since asset risk cannot be assessed and measured independently of market conditions and market sentiment. As a result, the need for capital will always be underestimated under favourable market conditions, leading to balance-sheet fragility and precipitous asset sales when market sentiment turns sour. Banks need a capital buffer to overcome the massive asymmetries of information between bank managers on one side, and investors and regulators on the other. This asymmetry currently makes it easy for bankers to accumulate excessive risks in the quest for higher returns, before markets become aware. The way to do this is to set capital requirements in straight proportion to total assets or liabilities of banking groups.

Fixing flaws in prudential capital rules does not remove moral hazard from the banking system, whose specific sources must be tackled separately. These are:

• the deposit-institution franchise,
• the implicit or explicit promise of bailout in case of threatened failure, and
• regulatory forbearance.

The problem associated with the deposit franchise is well known. If depositors have doubts on the bank’s solvency, they will run for the exit, forcing rapid liquidation of banks’ assets, possibly with large losses and contagion spreading instability to other banking institutions.

First pillar for tackling moral hazard

Deposit insurance can reassure depositors, and thus is the first pillar for tackling moral hazard, but it also mutes their incentive to monitor the management of their bank, since they no longer risk losing their money.

More importantly, deposit insurance has evolved in most countries into a system effectively protecting the bank, or the entire banking group, rather than depositors. When a bank risks becoming insolvent, supervisors step in to cover its losses and replenish its capital so as to avoid any adverse repercussions on market confidence. Moreover, most deposit insurance systems are inadequately funded by insured institutions, entailing an implicit promise that taxpayers’ money will make up the difference in case of failure of large banks.

Thus, in order to re-establish a proper price for the banking charter, banks should carry, ex ante, the full cost of deposit protection, making sure that in most circumstances the guarantee fund would be adequate to reimburse depositors when individual banks fail. Of course, no fund could ever be sufficient to meet a general banking crisis, but a fund of an appropriate size would offer adequate protection in normal circumstances, with only a predictable share of banks going bankrupt.

Deposit insurance fees are the right instrument to make banks pay for risk generated by banks. They should be determined on the basis of a careful probabilistic assessment of the likelihood of failure within the overall pool of deposits and risks of the banking system (within appropriately defined market jurisdictions). This is where the risk profile of banks’ asset and loan portfolios can be taken fully into consideration, together with, more broadly, the quality of bank management and risk control, thus creating effective penalties for riskier behaviour. Size itself could be appropriately penalised by higher fees that would incorporate a probabilistic price for the potential threat for systemic stability.

Second pillar: Not “too big to fail”

The second pillar required in order to greatly limit moral hazard in the financial system is credibly removing the promise that large banks cannot fail. To this end, all main jurisdictions should establish special resolution procedures – as already exist in the US and the UK – managed by an administrative authority, with powers to require early recapitalisation, manage required reorganisations and, once all this has failed, liquidating the bank with only limited systemic repercussions. Crisis prevention, reorganisation and liquidation would all be part of a unified consolidated resolution procedure managed for each bank, by one administrative authority.

In order to make resolution feasible, all banks and banking groups would be required to prepare and provide to their supervisors a document detailing the full consolidated structure of legal entities that depend on the parent company for their survival, the claims on the bank and their order of priority, and a clear description of operational – as distinct from legal – responsibilities and decision-making, notably regarding functions centralised with the parent company. This "living wills" document may also comprise "segregation" arrangements to preserve certain functions of systemic relevance even during resolution: for clearing and settlement of certain transactions, netting out of certain counterparties, suspension of covenants on certain operations.

Third pillar: No regulatory forbearance

Finally, the third pillar of an effectively reformed financial system is a set of procedural arrangements that will prevent supervisory forbearance. Supervisory discretion to postpone corrective action would be strictly constrained, so that bankers, stakeholders and the public would know that mistakes would always meet early retribution. To this end it is necessary to establish a system of early mandated action by bank supervisors ensuring that, as capital falls below certain thresholds, the bank or banking group will be promptly and adequately recapitalised. Should capital continue to fall, then supervisors should be required to step in and impose all necessary reorganisation, including disposing of assets, selling or closing lines of business, changing management, ceding the entire bank to a stronger entity.

Should this not work, then liquidation would commence. A bridge bank would take over deposits and other “sound” banking activities, thus ensuring their continuity. All other assets and liabilities, together with the price received for the transfer of assets to the bridge bank, would remain in the “residual” bank, which would be stripped of its banking licence. An administrator for the liquidation of the residual bank would be appointed to determine its value and satisfy creditors according to the legal order of priorities (based on the law of the parent company and other jurisdictions involved).

The attractive feature of mandated corrective action is that asset disposals and change of management will normally take place well before capital falls to zero, so that losses for the insurance fund and ultimately taxpayers are more likely to be much smaller.

References

Carmassi, E L, and Stefano M (2010), "Overcoming too big to fail - A Regulatory Framework to Limit Moral Hazard and Free Riding in the Financial Sector," CEPS-Assonime Report.

Gros, D (2010), "Too interconnected to fail = too big to fail: What is in a leverage ratio?", VoxEU.org, 26 January.

Republished with permission of VoxEU.org

On Financial Modeling and Risk Analysis

Here are some basic questions that I am frequently asked about financial modeling and risk analysis:

What is financial modeling...?

The process by which a firm constructs a financial representation of some, or all, aspects of the firm or a given security. The model is usually characterized by performing calculations, and makes recommendations based on that information. The model may also summarize particular events for the end user and provide direction regarding possible actions or alternatives.

What does financial modeling entail...?

Financial models can be constructed in many ways, either by the use of computer software, or with a pen and paper. What's most important, however, is not the kind of user interface used, but the underlying logic that encompasses the model. A model, for example, can summarize investment management returns, such as the Sortino ratio, or it may help estimate market direction, such as the Fed model.

What is risk analysis...?

The study of the underlying uncertainty of a given course of action. Risk analysis refers to the uncertainty of forecasted future cash flows streams, variance of portfolio/stock returns, statistical analysis to determine the probability of a project's success or failure, and possible future economic states. Risk analysts often work in tandem with forecasting professionals to minimize future negative unforseen effects.

What does risk analysis entail...?

Almost all large businesses require a minimum sort of risk analysis. For example, commercial banks need to properly hedge foreign exchange exposure of oversees loans while large department stores must factor in the possibility of reduced revenues due to a global recession. Risk analysis allows professionals to identify and mitigate risks, but not avoid them completely. Proper risk analysis often includes mathematical and statistical software programs.

Source: Investopedia

What is Risk?

If one accepts that probability theory is objectively valid and reliable, then drawing a distinction between what is knowable (probabilistically) and unknowable in matters of chance becomes instructive for informed decision-making in general. Prof Frank H Knight (1921) was speaking objectively when he proposed his infamous distinction between "risk" and "uncertainty." According to Knight (p. 233):

To preserve the distinction... between the measureable uncertainty and an unmeasurable one we may use the term "risk" to designate the former and the term "uncertainty' for the latter.

Knight's proposition contrasts with the earlier subjective views of David Hume (1748) who argued that chance is non-existent, therefore implying that the distinction between knowable and unknowable risks is a tautology. According to Hume (p. 469):

Though there be no such thing as Chance in the world; our ignorance of the real cause of any event has the same influence on the understanding, and begets a like species of belief or opinion.

The debate between the objectivist and subjectivist conceptions of risk continues to this day. However, the philosophical debate is more intense than ever given the reliance that modern finance makes of the objectivist approach to risk management and control (if that is even possible). I will not seek to resolve the two here. However, it behooves those intent on entering the field of finance to prepare themselves for this discussion. The future of financial economics may depend on your conclusions.

References:

Hume, D (1748/1952), An Enquiry Concerning Human Understanding, in R M Hutchins & M Adler (Eds), Great Books of the Western World (Vol 35, pp. 451-509). Chicago, IL: Encyclopedia Britannica.

Knight, F (1921/2002), Risk, Uncertainty, and Profit, Washington, DC: Beard.

The Flaw of Averages

For those who seek to "solve" the problem of risk through the calculation of a single numerical value (as in the mean, median, mode, variance, standard deviation, alpha, beta, power, value at risk, etc), I commend this video by Dr Sam L Savage, author of The Flaw of Averages (2009, John Wiley):



To understand and evaluate risk, decision-makers must consider a multitude of fundamental, descriptive, and inferential measures and indicators in comcomitance. The potential for misspecification (as in model risk) and misinterpretation (as in naivety) are great. Thus, risk management (including analysis) should be viewed and treated as a polyvalent task that defies commoditization and delegation in enterprise.

Embedded versus Embodied Decision Support

Within the realm of decision support methodologies, two very different paradigms are vying for the attention of enterprise. The first is what I call the embedded approach to decision support. The embedded approach to decision support emphasizes scientific logic and rigor, and is grounded firmly in the traditional disciplines of operations research, systems analysis, decision analysis, and risk analysis[1].

Embedded Decision Support Methodologies

The second still emerging approach is what I call the embodied approach to decision support. The embodied approach to decision support traces its roots to the information technology movement and enjoys critical acclaim for its potential for automated performance monitoring, business intelligence, and business analytics.

Embodied Decision Support Methodologies

Note that the content-validity of the embedded approach is widely accepted amongst professional researchers and analysts as a body of knowledge. The literature underlying the embedded approach is vast and rich in empirical evidence supporting the validity and reliability of its methods. This extant literature regarding the embedded approach is synonymous with the disciplines of operations research, systems analysis, decision analysis, and risk analysis.

The content-validity of the still emerging embodied approach remains in question. Researchers and analysts are still debating many of the terms used in the embodied approach, and a broad consensus regarding what exactly business intelligence and business analytics entail is not yet evident. The existing literature supporting the effectiveness of embodied methods is mostly descriptive with scant empirical evidence to support its validity and reliability as a proven decision support methodology.

The significance of the differentiation between embedded and embodied methods lies in the warranties that each provide the decision maker. An impressive quality of the embedded approach is that all the terms and concepts used are clearly defined and widely accepted by professional researchers and analysts thus enabling users to articulate universally their findings and recommendations.

In contrast, the lack of consensus regarding the validity and reliability of embodied methodologies limits the utility of what we know to be business intelligence and business analytics. Indeed, the methodological frameworks for business intelligence and business analytics are still emerging in the form of dashboard reporting systems and other untested visualization methods that some researchers argue can lead to cognitive distortions of the evidence uncovered by such methods.

Future consilience between the practitioners of embedded and embodied methods is far from complete or even certain. More empirical evidence will be needed before the embodied approach can be fully converged or enjoined within the deeper conceptual foundations of embedded methodologies. In the mean time, decision makers are advised to take precautions to ensure that embedded methodologies take the lead in verifying and confirming the findings and recommendations of embodied technologies.

[1] Note that risk analysis is not to be confused with risk management, which is a different function and discipline all together.

We the People Need to Know What is Happening and Why…

With the US investigation of what happened at BP and its failed deep-water oil rig only beginning, the public should not be shy in its demands for full disclosure of the evidence and findings. The BP oil spill has placed society on notice that egregious engineering and risk management decisions are placing our nation and world increasingly at risk. I therefore echo the call by Dr Nansen G Saleri of Quantum Reservoir Impact for a thorough public investigation into the BP oil spill disaster:

What is needed is a scientific, thorough and apolitical investigation headed possibly by the National Academy of Sciences and drawing in experts from the oil and gas industry as well as the government agencies involved. The investigation must also evaluate the entire post-accident response effort led by BP in cooperation with local, state and federal agencies.

Some questions that must be diligently probed by investigators are: 1) Why did the blowout preventers—the massive valve assemblies designed to stop an uncontrolled flow—fail? And what are their reliability statistics? 2) Were the redundant safety systems truly redundant? It seems obvious they weren't, but this has to be verified. 3) How well trained was the crew? 4) Were the safety systems and contingency plans in place commensurate with the immense values of the total assets at risk—human, material and environmental? 5) Did operational and cost-cutting practices compromise safety?

The ecological and environmental catastrophe now underway in the Gulf of Mexico, coupled with the ongoing monetary crisis that has gripped global markets, are clear evidence that the decisions of governments and multi-national corporations are placing society and our world increasingly at risk. We the people need to know what is happening and why…

Source: Saleri, N G (2010, May 7), Learn From the BP Disaster, Then Drill Again, Wall Street Journal Online.

The Five Neglects of Risk Management

Here is a useful list of five "neglects" that we as risk analysts must become versed in correcting prior to disaster (Berger, Brown, Kousky, & Zeckhauser, 2009):

1. Probability neglect (consideration of probability)
2. Consequence neglect (valuation of potential benefits and losses)
3. Statistical neglect (accurate use of subjective probability and statistics)
4. Solution neglect (delineation and evaluation of all available alternatives)
5. External risk neglect (incorporation of all benefits and costs accruing to the decision maker)

Catastrophes can and should be instructive for future practice...

Source: Berger, A, Brown, C, Kousky, C, & Zeckhauser, R (2009), The Five Neglects: Risks Gone Amiss, in H Kunreuther & M Useem (Eds), Learning from Catastrophes: Strategies for Reaction and Response (pp. 83-99). Philadelphia, PA: Wharton School Publishing.