Amaranth Advisors lost roughly $5 billion in a week, and this is from a hedge fund that boasted of world-class risk-management systems. The result is a loss of 50% of the company’s asset base best summarized by this USAToday headline Faced with billions lost, Amaranth Advisors will shut down.

Amaranth Advisors was described as increasingly brash in their investments due to their confidence in their quantitative approach to risk management. According to this article in Business News, “The risk models employed by hedge funds use historic data, but the natural-gas markets have been more volatile this year than any year since 2001, making models less useful. They also might not predict how much selling of one’s stakes to get out of a position can cause prices to fall.” The Amaranth Advisors risk culture also had its roots in convertible-bond trading, a less-volatile market.

Enterprise Risk Management (ERM) best practices add a forward looking and scenario based approach for a more balanced and comprehensive view of risk. ERM is a process comprised of a series of iterative and sequential steps to enable continuous improvement in decision-making and performance with regards to the reduction of uncertainty within an organization. ERM helps a management team examine the markets in which it operates and formalize the acceptable risk tolerance for each segment. This process-driven approach helps a company set more appropriate controls to bring the business in alignment with the established risk appetite. This approach addresses the root cause of potential future problems rather than monitor transactions for historic symptoms.

The Amaranth Advisors outcome is a classic case that demonstrates the pitfall of an overly quantitative approach to risk management. Companies that have an over reliance on the traditional quantitative approach to risk management, namely the use of automated triggers based on data analysis to control risk, is much like the Emperor in the fabled children’s story who believed too heavily in just one approach for the source of his information.

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The book “Blink” by Malcolm Gladwell is a must read for risk managers. Chapter one opens with the description of the approach used by the J. Paul Getty Museum to perform due diligence on a famous statue’s authenticity prior to acquisition for their collection. This is a classic low frequency and high impact event with a price tag of $10 million for the statue. They hired a team of consultants and lawyers that did deep analysis. For example, a geologist determined the marble used for the statue was consistent with the statue’s origin and a legal team did a paper trail that validated the chain of ownership. After 14 months of investigation, the Getty Museum staff with the help of professional consultants concluded the statue was authentic, and the Getty Museum made their purchase.

However, when the statue was shown to art experts their conclusions were immediate that it was a fraud. These art historians sensed that although the statue had all the obvious telltale signs that it was genuine, their instinct told them it was a fake.

As a result, the investigations were revisited and the holes began to appear in what was previously determined a rock solid conclusion. Eventually, the statue was revealed to be a forgery dating back to Rome in the early 1980’s. How could 14 months of rigorous due diligence by highly trained and paid professional consultants be wrong? So wrong in fact, that art historians who relied on their instincts could come to the correct determination in a matter of moments?

The author, Gladwell, argues in his book, a powerful process in all of us is working subconsciously to sort through huge amounts of information gathered over a lifetime, make associations between data, and extract key indicators to arrive at rapid highly accurate conclusions.

This is also the process of Enterprise Risk Management (ERM). A few ERM best practices are illustrated in this story:

• Let your line management lead the risk management process for their areas.
• Capture this expert opinion with a framework of risk indicators and a root cause discipline to ensure the quality of capturing the expert opinion.
• Document their self-assessments of their operating processes to identify “What could go wrong?” based on their powerful expertise gathered from intimate knowledge of the subject matter.
• Evaluate the expert opinion to determine if action needs to be taken.
• Formalize the mitigation process to follow-up on these instincts to craft a plan of action that takes into account historical data and traditional analysis.
• Monitor the plan of action to make sure it actually achieves the goal rather than just appearance.

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Whenever there is a disaster or event that causes losses, it is usually proven that someone or several employees in middle management or on the front lines had been forecasting the event years before but no action had been taken. The recent story of British Petroleum’s oil pipeline leak in Alaska is no different. The headline from the CNN news story, BP was warned, this week reads “Interviews with employees and a 2002 letter predicting 'catastrophe' show that BP’s problems should have come as no surprise to management”

According to the article, “One current BP employee who worked at both Prudhoe Bay and in Texas and spoke to Fortune on condition of anonymity says no one should be surprised by what eventually occurred. "The mantra was, Can we cut costs 10 percent?” he recalls.

How can such bad decision making be made by such smart people? The answer is found in the over reliance on quantitative analysis. There is a philosophy among some risk managers that all answers can be found in the deep quantitative analysis of the numbers in databases to detect patterns. This is true for high frequency risks. However, for low frequency and high impact risks (like the BP oil leak) quantitative analysis will often lead to incorrect decision making or more analysis with no decision making at all. First, there is insufficient data historically to analyze and many possible outcomes can easily and incorrectly be “fit to the data”. Second, with too little data, the patterns of correlation, dependency and therefore big picture ramifications can not be easily understood.

The solution is Enterprise Risk Management (ERM). ERM is an iterative and sequential series of steps that utilizes risk self-assessment (the process of identifying and evaluating risk with regard to their potential impact and likelihood, as well as related controls) as well as the subsequent risk management process of control evaluation, action plan definition, monitoring of risk- and implementation development. Enterprise Risk Management starts with a holistic and qualitative approach to first identify all the possible root causes of an issue and then systematically help quantify the total risk consequence taking all the possibilities into consideration with scenario analysis and if needed quantitative analysis.

Quantitative analysis is expensive and very focused in applicability. Enterprise Risk Management is all about best practices of performing a self-assessment and scenario analysis before deciding where, when and how to invest in an deeper quantitative analysis like loss database approaches. With ERM, management can prioritize the full costs versus the benefits to make a better decision. You can download a whitepaper on Risk Event Classification. Click here to download.

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