Category: Strategic Risk

Risk Predictions for the New Year and Interesting Business Cases in 2016

Risk Predictions for the New Year and Interesting Business Cases in 2016

Risk Predictions for the New Year and Interesting Business Cases in 2016

With 2016 just about a week old, I would like to share some risk issues that are interesting to consider in 2016.

Federal Reserve Interest Rate Increase

Although delayed many times, the Federal Reserve did finally increase the benchmark interest rate.[1] However, inflation does not seem to exist in a significant manner and some sectors, like energy and commodities are seeing deflation. There has already been discussion of the Federal Reserve slowing its rate of interest rate increases.[2] The recent move to increase interest rates is a real test and vote of confidence that job expansion and growth are happening and will continue to happen.

I think the underemployment situation is a major risk to the economy that hinders growth prospects. The U6 measure of underemployment, which accounts for those unemployed and underemployed, as tracked by the US Bureau of Labor Statistics, ticked up in November 2015 to 9.9% and remained there for December of 2015.[3][4] Although it is on a long-term downward trend from the peak in 2010, the level of 9.9% is still at levels seen in 2008 and 2002, when we had major recessionary pressures, suggesting the job market is still delicate. With many baby boomers existing the job market, the hope is that even a sluggish job growth in the economy can suffice to bring job growth.

Even with the seemingly strong job growth numbers in the recent December labor report, there is a strong concern of wage stagnation. “Wages simply did not grow, and with Wall Street expecting a 0.2% increase in average hourly wages, in December not only was there no wage growth, but in fact, average hourly earnings posted a tiny decline from $25.25 to $25.24.”[5]

Outlook and Prediction: Look for the Federal Reserve to reverse course by the third quarter of 2016, either by reducing rates or increasing liquidity through more quantitative easing. The first week of 2016 had already shown that there are many forces working against wage growth.


Most of the world remains in shock that the seemingly disorganized militants and extremists can pull off the attacks seen recently. The reality is that they are more organized and funded than we admit in the media. With the recent San Bernardino attack, it is clear that ISIS is a growing and formidable risk for the US and all western countries.

Outlook and Prediction: ISIS will continue to dominate the US presidential election as a major concern. The ability to respond to this risk will command attention of the US voting populous. Nationalistic feelings are on the upswing in Europe and even in the US, as demonstrated with the swelling support for Trump. Actions by ISIS and the role of the US in the region will be important to voters. Continued terrorist actions by ISIS lift Trump, too. It will make for an interesting and risky election, especially for candidates that look soft on or inexperienced with terrorism.

Deflation (In Various Forms and Places)

The danger that economist all try to avoid is that of deflation. Deflation is great if you hold cash, as assets become cheaper in the future, but for economies built on consumerism and borrowing, it is really bad news and leads to default and lack of investment (See Irving Fisher for his great work on this topic). Deflation is alive in Japan.[6] Consumer prices fell in Europe in late 2015, showing signs of the deflation beast, even with the massive quantitative easing at work in Europe to ward it off.[7] Here in the US, we have seen deflationary trends, largely driven by the drop in energy prices. China has shown signs of a slowing economy, and demographic realities of an aging US, Europe, Japan (and even China) are deflationary forces that might be kicking into high gear. (See my recent posts on China’s changing Demographics and the The World in 2050). A real danger is that as deflation takes hold, firms can and will pay less for labor and with underemployment already high, the risks of salary stagnation or even salary deflation is growing in the US and globally. (Ask the people working in the energy sector or even on Wall Street. Ask what bonuses get paid out this year, too.)

Outlook and Prediction: Hopefully, low energy prices will stimulate growth in various sectors, but it has not seemingly happened yet. Low energy prices are typically seen as an opportunity to expand, but much of the economy is driven by services that are not directly related to energy. There is some additional benefit to lower energy prices, still. Paying less for energy may allow consumers to spend more elsewhere or save more, which is generally seen as good. Currently, holding cash does not offer a large penalty now for investors, which already sounds like deflation, so stimulating saving over spending is not too helpful now. Look for firms to hold hiring increases in 2016 and for deflationary issues to weigh on salary growth.

Some interesting firms were in the news in 2016, offering lessons on the handling of risk and how they will recover from the risks seen will prove very interesting. Here are a few to consider:

Cars: Volkswagen, GM, Tesla, (Plus Gas Prices)

The cheating and deceit at VW is only surpassed by the ignition scandal at GM (See post on The Value of Trust and Leadership Failures at VW). In the case of GM, knowingly installing faulty ignition switches harmed and killed people. GM will try to get its cases resolved quickly, but a divided Congress and an election year can make it a bigger mess. Which politician will stand with GM’s position this year? Few if any, I predict. With the concurrent VW case unraveling and everyone vilifying VW, supporting the GM case just got harder for GM (or anyone else like politicians or unions). Expect expensive settlements soon. As for VW, it  has cheated customers and the environment. Its position as a progressive, labor-forward, and environmentally oriented firm is damaged for a long time. There will be fines, maybe some prison times for executives, and a period of apology. People will remember this for a long time.

Outlook and Prediction: VW will survive, but its image as the progressively minded car manufacturer is tarnished and it will take a big hit in credibility. VW has lost the halo of being the environmentally minded and labor-forward firm that can teach others how to operate. VW sold a lie. BTW, who needs diesel cars now? Gas is at early 2000 levels (See graphic below from the EIA).[8] In the absence of an environmental promise on clean diesel, what is the benefit of a diesel car? While you are trading in that diesel VW, get an electric car, like a Tesla – that is what all the uber progressives want anyhow. Tesla can’t make cars fast enough!


This darling of the fresh, humane, fair, and sustainable food movement has hit more than a few bumps in 2015. The shortage of carnitas (due to a lack of pork raised by humane practices) was handled very well by management. Some might even say it helped Chipotle strengthen its sense of ethos and trust with consumers. However, the recent and multiple E. coli outbreaks at Chipotles across the US have raised concerns about the firm’s handle on its supply and operations. Can these guys keep the food safe? The stock analysts thought the burritos were a bit too hot and beat the stock down. Chipotle’s stock is down big time, losing the heat a rate of nearly 30%![9]

Outlook and Prediction: The food industry is driven by fickle tastes. Is Chipotle a movement, like Starbucks and Panera Bread, or is it a fad that will be beaten back by changing tastes? The safety issues will test consumer demand for Chipotle, and lots of options exist for consumers to get their burrito fix elsewhere. I think Chipotle will bounce back. But it will require that management really gets a handle on its sourcing and operations. It will further require a strong demonstration that things are different going forward and that the risks are under control. It will be a great example to exercise the benefits of risk and supply chain management to return to its pepper hot place in the market.


There seems to be no sport that has ever enjoyed the dominance currently enjoyed by the NFL. Fans are engrossed each weak, gambling (no, um, playing games of skill) for weekly payouts. But the NFL has major issues facing its image. Last year, the high profile cases of players assaulting women made the news. This year, the release of Concussion, a movie that highlights the concussion risks in the NFL, will, in my opinion, cement in the minds of millions that football is a real health risk (and avoidable). Added to this, we have seen various health warnings on playing youth football and challenges by doctors in recommending not to play the youth sport.[10] [11] A recent survey of views of parents by NPR shows that 51% believe high school football is too risky or needs to be made safer (See graphic below).[12] It is not a good sign for the creation of the next generation of NFL players.

With the NFL owners and teams desiring more luxurious stadiums in various markets and with local municipalities all suffering economically from the downturn in tax revenues since the Great Recession, the appetite for municipally financed stadiums is on the decline. In a major twist, the stadium proposed for relocating the St. Louis Rams to LA would be financed by the owner. This is a change in course for teams and will give some cities increased leverage when teams demand municipally financed stadiums in the future.

Outlook and Prediction: The NFL has a major liability with concussions. Indeed, it has (and likely will) pay for the health damages experienced by past players. But its impact to the image of the sport and the cultivation of younger fans is a growing risk. How many kids go into boxing? The NFL can be experiencing a similar downturn.

The role of cities in funding stadiums is a potentially alienating risk, too. Although not sanctioned by the NFL, the gambling sites for the sport offer long-term risks. Will football be about the sport or the payouts in fantasy football? The answer will change the make-up of fans and what they want out of the sport. Gamblers are less likely to follow one team, I think, making the franchise model less valuable to owners. And, the growing public image of a game destroying people with concussions and municipalities supporting this with stadium subsidies will become a challenge to NFL advances on the stadium front. I think the recent movie, Concussion, will galvanize people to believe more strongly that football is dangerous and that other people should play it and other people should pay for it, but they will continue to watch the product on TV.

Happy New Year!

About Russell Walker, Ph.D.

Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.

His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.

You can find him at @RussWalker1492 and

[1] Fed Raises Rates after Seven Years Near Zero, WSJ, Dec. 16, 2015.

[2] Fed Raised Rates Even as Inflation Debate Continued, Reuters, Jan. 6, 2016.

[3] U6 Unemployment Rates (2000-2015)

[4] The True Unemployment Rate, U6 vs U3.

[5] December Jobs Soar by 292k, Smash Expectations, but Average Wages Post First Drop Since 2014.

[6] Japan Falls Back into Deflation for First Time Since 2013. Financial Times, Fall, 2015.

[7] Eurozone Faces Renewed Deflation Threat as Consumer Prices Fall. WSJ, Sept. 30, 2015.

[8] US Historical Gasoline Prices from EIA.

[9] Chipotle Stock price for 2015, CNN Money

[10] Noted Surgeon, Dr. James Andrews wants your young athlete to stay healthy by playing less.

[11] America’s Most Dangerous Football is in the Pee-Wee Leagues, Not the NFL. The Atlantic. Aug. 2013.

[12] NPR Poll on High School Football Safety, Feb, 2014.

Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

This year, Florida has experienced its 10th consecutive year without a hurricane. It is the longest period without a hurricane strike in modern times and one more remarkable considering that Florida’s more then 1200 miles of coastline account for about 40% of the US landed hurricanes recorded in modern history.

Exploring this long stretch without hurricanes is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization. First, the obvious: how frequent are hurricanes and are hurricanes regular in their arrival? The below graphic from the WSJ of last year nicely shows this.[1]

Indeed, the graph shows that in some decades such as the 1910s, 1920s and 1940s, hurricanes were quite frequent in Florida (nearly annual!). Interestingly, the frequency of hurricanes is less in recent decades, except for a major spat of hurricanes in 2004 and 2005. All of this raises questions that are of great interest to climatologists, disaster recovery planners, risk managers, insurers, and re-insurers. Is the irregular arrival of hurricanes just a manifestation of randomness?

It might be a product of climate change, global warming, or simply a level of variation in the natural cycle not seen before. Indeed, hurricane patterns are complex, and we are rapidly learning more about their formation and occurrence, thanks to improved data collection and analytics. In recent years, climatologists have been able to zero in on factors that are more predictive of high hurricane activity years. The below graphic from NOAA communicates some of the most important factors in a high hurricane occurrence year.[2]

A large number of hurricanes are expected when there is high pressure in Northwest Africa, warm temperature in the Atlantic, and favorable trade winds. That is a complex interaction of variables. And, El Niño is generally shown to result in less of these conditions and less therefore hurricanes. Such insight is valuable to a risk manager and risk insurer. From a risk management perspective, knowing about this beforehand allows for more appropriate risk taking, preparation, and investment. Indeed, owning hurricane insurance risk in Florida over the past few years turned out to be a rather nice investment.

Having grown up in Tampa, Florida, I was acutely aware of the dangers and damages from hurricanes – at least I had heard about hurricanes from my grandparents. In some 22 years in Tampa, I saw only one hurricane come by Tampa in 1985. The interesting phenomenon was that the west coast of Florida had seen many hurricanes in the 1910s and 1920s and then a scrap with a category 5 hurricane in 1960. This irregularity in hurricane arrivals perplexed me. I can recall fishing in the inter-coastal way and seeing passes and breaks formed by hurricanes from the past. Why were there less hurricanes in the 1970s and 1980s than in previous decades? Or why were there more in the past? Did something change?

Risk Management Lessons

This phenomenon interested me so much that I explored it as part of my PhD Dissertation at Cornell University.[4] At the time, we did not have the big data tools of today. In particular, I examined if hurricanes and other large flood events were indeed irregular in their arrival. I found that for the southeastern US, large annual flood events are statistically “clustered in time.” That is to stay that some periods of time show many large annual floods and then there are extended periods of time (many decades) with little to no large floods at all. It is a major finding that challenges the principle assumptions of catastrophic risk analysis. It suggests that risk is dynamic and the underlying assumptions subject to changing conditions.

If the 100-year flood comes on average once every 100 years and it has been seen two times in the last 10 years, it also might mean that a long period of tranquility is ahead. That can potentially be exploited by insurers in issuing insurance during low risk periods. The recent Florida hurricane data suggest that such changes are indeed part of the climate.

Key Point: Risk models are simplifications of the real world. With more data, we can explore, understand, and account for relationships across many variables. Big data analytics is changing how we examine risk, not just in climate, but in finance and healthcare, for instance. Deploy Big Data analytics to leverage large scale and multi-variable data sets to understand risk more precisely.

Data Visualization Lessons: Risk is Dynamic and Complex

I came across a great graphic made by John Nelson of IDVSolutions. [3]

It graphs hurricanes and tropical storms since 1851 and uses colors (more green is more severe) to show the severity of hurricanes and the progression of the hurricanes along their tracks. With little explanation or climatic training, you can easily see some interesting things about hurricanes in the Gulf of Mexico and Western Atlantic Ocean. At least for me, I see a rather suspicious blank space in west Florida – an indication of less or at less severe hurricanes than other parts of the state and the Gulf Coast. One explanation is good luck; another is more physical in that hurricanes lose strength over land. It is hard to hit the west coast of Florida without hitting some land first. So, the west coast of Florida, may, in particular, be more protected. It might be useful in selecting risks. Buy hurricane risk on the west cost of Florida over the Miami area.

The next observation of this impressive data visualization by IDV Solutions is that the strongest hurricanes do in fact avoid land in their formation, riding through the Florida Straits, skirting south of Cuba, and otherwise strengthening in the Gulf of Mexico. And if you ever thought that New Orleans and the Louisiana Gulf Coast gets more strong hurricanes that elsewhere, this graphic would support your hypothesis. This is a great example of a data visualization that allows for the communication of hurricane tracks, relative strength, and geographic occurrence. It would have been great to have this when we looked at hurricane and flood data some years ago.[4] It is a great example of how data visualization is changing analysis. In a few minutes, complex relationship between location, direction, intensity, and reporting can be understood. It is also a great example of why we need data visualization as part of our analytical and risk toolbox.

We, as humans, cannot easily process complexity in numbers. However, we (or some of us in particular) are quite good at addressing and processing complexity expressed in shapes, colors, and graphics. This strength and weakness of our cognitive skills requires that we be mindful of how to use data visualization as part of an analytical strategy. It makes sense and I am reminded of this every time I park on the purple level of the parking garage at Northwestern University. I can’t as easily recall the number of the level at the garage, but the colors of each level are crystal clear to me in memory, and yet I use the garage nearly everyday. Numbers although necessary for analytics, are not the best form for our cognitive processing. Relative comparison and rate changes are more easily understood through graphics.

Key point: Leverage data visualization to understand and explore complex relationships across many variables in data. Leverage the human mind to look for patterns and ask interesting questions of the graphics. It leverages the best of graphics and our cognitive skills.

About Russell Walker, Ph.D.

Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.

His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.

You can find him at @RussWalker1492 and


[1] Jakab, Spencer. “Florida’s Hurricane Dry Spell Lasts.” The Wall Street Journal. November 28, 2014.

[2] Klotzbatch, Phil. “Forecast groups nailed the 2015 hurricane season, thanks to El Niño.” Washington Post. November 25, 2015.

[3] Nelson, John, Hurricane Data Visualization, Accessed November 27, 2015.

[4] Walker, Russell. “Risk and Statistical Analysis of Hydrologic and Environmental Data.” Ph.D. Dissertation, Cornell University Library, August 1999.

[5] Walker, Russell and J. R. Stedinger. “Long-term Variability in the Arrival Rate of Flood Events as Evidenced by Flood Clustering.” EOS Transactions, American Geophysical Union 2000 Spring Meeting, 81(19), S200, May 9, 2000.

Risk Management Leadership Lesson: The Value of Trust in Operations

Risk Management Leadership Lesson: The Value of Trust in Operations

Risk Management Leadership Lesson: The Value of Trust in Operations

In business and life, we grow to expect certain things. Namely, our society expects companies to produce products that are safe and reliable. We go to Yelp and rail against restaurants that do not meet our expectation for service. However, large firms, when caught red-handed often have gotten by with a mere slap on the hand. When we see a firm misbehave or use a controversial advertisement, we see boycotts initiated and apologies extracted. What about more severe damages? How a firm operates is important in its success and in forming trust with its customers.

In the last few weeks, we have seen a couple of major developments in how firms have cheated and thus lost trust. Stewart Parnell, the former CEO of Peanut Corporation of America, was sentenced to 28 years in prison for knowingly selling and distributing peanut products containing salmonella. At least nine people are known to have died from these contaminated peanut products. It is a striking case, because we now have the science to keep food safe. We now have the science to find what has killed us and identify the source of that contamination. Yet, a firm and its executives decided to operate in a reckless manner. It is the first severe penalty levied on a food company for selling contaminated food. In the trial, former employees of the Peanut Corporation of America testified that the CEO and firm prioritized profits over safe operating conditions. Of course, the tragic deaths cannot be reversed with prison time or fines. The damage to the Peanut Corporation of America was self-inflicted. No competitor or market force did that to them. No surprise in the capital markets or fear of peanuts by consumers brought them harm. When firms cheat and do harm, they ultimately hurt themselves. This fraud is of course a major risk to shareholders, customers, markets, and, in this case, the health of people.

The recent EPA disclosures about how Volkswagen has more or less gamed its diesel engine systems to perform well on emissions tests (and only during tests) showcases yet another case of internal fraud. Attorneys General across the US are already calling for billions in damages from Volkswagen. The firm created an image for “clean diesel,” sold it to well-educated and wealthy Americans, who wanted an environmentally palatable vehicle, and they profited handsomely from it. Now the lies have been revealed. The fraud, again, is internal and self-inflected. No competitor, regulator, customer, or market force made Volkswagen do this. It is risk that now will harm shareholders, customers, the German economy, and the environment. And, let’s not forget about Toyota and its accelerator, GM and its ignition switches, and well… the list goes on and on. We lose trust in firms because of the harm they cause and because that is the result of internal risk taking and decision-making gone awry.

These two recent cases are largely about internal fraud. It is clear that the firms knew about their misdeeds and elected to operate in a reckless and harmful manner. We often think of internal fraud as a banker walking out of the vault with gold bars. Such fraud is far less likely to occur than that of an executive taking undue risk against the firm to meet short-term goals. With average CEO tenures on the order of 5 years, the pressure to preform is high and the window of opportunity is short. The threat of internal fraud is a risk that all firms must address.

The management of such risk falls under Operational Risk Management. Operational Risk and self-inflicted damages are the cause of the greatest reputational harm. Nobody forced BP, GM, Volkswagen, Toyota, or the Peanut Corporation of America to do what they did. Their executives elected to take risks (and dangerous ones). Trust requires operating successfully over many transactions and creating value for customers. Once that trust and reputation are damaged, the firm must work to change not only its image, but also its operation. The process to managing Operational Risk requires a treatment that addresses the organization, its culture, its management, and leadership. We will explore all of these topics in the upcoming course Operational Risk Master Class: Measurement, Management, and Leadership.

Join us!

About Russell Walker, Ph.D.

Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.

His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.

He  has advised many leading institutions on Operational and Reputational Risk Management, including: The World Bank, SEC, Genworth, Capital One Financial, Discover Financial, PNC, The Bank of England, and the US State Department, among others.

You can find him at @RussWalker1492 and

Winning with Risk Management

Winning with Risk Management

Traditionally, organizations have viewed risk management as a corporate requirement, and have often positioned it along with audit and regulatory functions. Some have even empowered and titled corporate groups to “manage risk” along these lines. This charge has often revolved around managing insurance policies and reviewing reports from rating agencies, which suggests that risk management was viewed more as the hedging of certain risks and the overall outsourcing of critical risk analysis, especially as related to credit risk. The recent economic downturn has shown a new face and place for risk management. The strongest firms in this economic downturn are those who integrated risk management as a more comprehensive part of corporate strategy. The weaker firms almost entirely shared the traditional risk management school of thought mentioned above. This is true in financial services and extends to nearly all industries reliant on credit, market, and operational risk management.

In the recent economic downturn, a few key behaviors of risk management as a driver of corporate strategy have emerged. First and foremost, sound risk management requires executive involvement and ownership. Next, there must exist a culture and climate for openly communicating risk in the organization. Additionally, communication of risk must have an emphasis on data-driven decisions. Lastly, but perhaps most critically, is that the organization must have a “ready response” to a known risk.

Let’s look at how executive involvement and ownership have a role in risk management in driving corporate strategy. A nice example is JP Morgan-Chase. Of the major banks in the US, JP Morgan Chase has carefully skirted the largest issues afflicting its competitors and brilliantly executed a strategy that is rooted in understanding its risk and adapting as needed. We can’t forget their buying of Bear Stearns at $10 a share and their buying of Washington Mutual (formerly the largest savings and loans operator in the US). It is worth looking at Jamie Dimon, Chairman and CEO of JP Morgan Chase. Unlike many a CEO, he took an active role in regular risk briefings. Not only did he ask for detailed risk reports as the CEO, he also recognized the need to set a direction for the organization in reaction to these risk outlooks versus delegating the risk decisions. When the investment banking industry was moving towards greater real-estate investments and larger CDO purchases, he looked to data from the JP Morgan retail banks that showed that mortgage defaults were on the rise, and he provided his team the direction (driven off of data) to move against the herd by selling real-estate backed securities. It is hard to fathom that an organization in the world that would make take such a drastic decision about risk without the direct involvement of its senior leadership. Therefore, just as executive involvement is important in setting corporate strategy, it is equally important in risk decisions.

To be effective as an organization, there must be honesty and openness in communicating risks. It is clear that the international real-estate bubble was in part fueled by a field of mortgages that were, in various forms, deceitful, incomplete, or otherwise untraditional. Indeed, the classically-trained credit risk managers signaled these mortgages as high risks. For many organizations that were focused on short-term earnings and felt a need to outpace the industry in bookings, this communication of risk was dismissed, or worse, even silenced. In the JP Morgan Chase example, it was the retail banking division that shared data with the investment bank on the escalations in mortgage delinquencies. This sharing of data across business lines allowed Mr. Dimon and his corporate team to change strategy on the investment side. For many organizations, sharing unexpected information is unwelcomed. Presumably, other banks could have done the same as JP Morgan Chase, but the focus on communicating risks and data across business lines was not there in other banks. The lesson, of course, is that an enterprise must be willing to communicate about risk, especially when things are going well and the risk has yet to be realized. Businesses lines should take time to learn what other lines are doing given the interconnectedness of risk within an organization.

The importance of information in risk management should not be missed. In recent months, many risk managers have pondered how the traditional risk management models failed to predict the crisis, as a great body of thought has gone into the development of the risk models and techniques that have been used to conventionally manage risk. In that convention resides the problem. Such conventional risk management techniques use historical data to make projections about “worse cases” or statistical anomalies that might happen with some likelihood. However, future negative outcomes are unknown to the models and future “failure paths” are unincorporated in the models. Most of the employed risk models are poor at incorporating new information and even worse at new types or sources of information, such as changes observed in a tangential business line, observations from front-line staff or traders, or alternations in market behavior due to phenomena such as reduced availability of capital.

In the case of JP Morgan Chase seeing signals in their mortgage accounts, they incorporated information on mortgage payments that was unconventional for the evaluation of portfolios of mortgages by the investment bank. Their success came from identifying such novel information and realizing that it challenged conventional thought. In such conditions, relaying on conventional risk models is highly questionable and some would even say harmful. So, the focus of a risk manager should not be strictly quantification (as in the execution of conventional risk models), but the identification and incorporation of information, especially from of new types and of new sources, in order to determine direction and changes that drive risk. Risk management is inherently a process of investigation and learning, rooted in unraveling the complexity of the unknown.

The risks facing organizations are legitimately more complex and tightly connected than ever before. The complexity of risk is largely driven by the continual globalization of business and the increased speed of virtually every business activity, as enabled by technological advances. Using data to make decisions is key; it enables verification, and provides a means of breaking down the complexity of business. For many organizations, there was a reliance on securitization or swaps to transfer risk in ways that were not possible a few years previously. This was heralded, and in fact, there are benefits to these instruments. In many ways, these swaps served as insurance, yet the buyers of such swaps were not necessarily qualified or even financially guaranteed (as is required by many a insurers worldwide). It is clear that very few of the buyers or sellers of such novel financial instruments understood the inherent interconnectivity of risks in these instruments. For instance, the US government is still unwinding the trades and obligations of AIG, which relied heavily on swaps and risk transfers. The case of AIG shows how even a large and diversified firm can struggle to fully understand its obligations and risks. Many firms like AIG, relied heavily on hedging or transferring of risk as a means of risk management. The assumption that risk is perfectly transferred means that one’s counterparty is perfectly resilient, too. This is of course a naïve view and one proven wrong recently, but one that fundamentally demonstrates how a few assumptions about risk can drastically impede a corporate strategy.

Still, in each corporate strategy, particular risks are accepted, namely and ideally those risks which management believes hold some attractive opportunity. Focusing on the data or factors that foretell of the risk accepted is key; it is how one begins to understand a risk and reduce uncertainty. Risk management is a process of investigation and study. Interestingly, many companies worldwide have accepted data at face value, such as credit ratings from the agencies, the financial stability of a counterparty that was buying a swap or credit risk transfer, or the direction of commodity or real estate prices. For example, it is clear that the US automobile industry was not prepared for the recent volatility in oil prices. The “Big Three” manufacturers were largely working on a view that oil would remain inexpensive to the US consumer. Instead, the likes of Toyota and Honda were making calculated investments in hybrid vehicles and other high efficiency vehicles to position themselves for an upswing in oil prices. In many ways, Toyota and Honda, had already “readied their response” to the risk posed by higher oil prices and the subsequent impact on their customers. This reflects a treatment of risk on the part of Toyota and Honda as part of their corporate strategies.

This forward thinking about risk is key in organizations. Toyota and Honda were not immune to the recent economic downturn nor did they completely depart the previously lucrative SUV market in the US, but clearly each was better positioned than the major US manufacturers, because they were better prepared. They identified a risk, took action in a way that would allow their corporate strategy to adapt to an environment with lower consumer interest in large vehicles. The emphasis is on “readying the response,” much in the same way that militaries conduct simulations to prepare for a yet unseen conflict. Companies that ready a response for various situations are not necessarily better at predicting the future; they are just more prepared for what comes to pass. This continuous preparation often makes them better at understanding factors predictive of a risk. So, being ready is not preparing for doomsday, but rather being able and prepared to adapt.

It is interesting we have heard the phrase “liquidity risk” come to describe the woes of many a firm recently. In fact, it is a more polite way of saying that an organization ran out of money. The seeds of today’s liquidity risks were set a few years ago, during more prosperous times, when companies dispersed excess cash through dividends, share buybacks, and undertook a wave of high-priced mergers. Indeed, shareholders and the investment community clamored for this sharing of wealth and punished those firms that held “excessive cash reserves.” Yet, today those organizations that hoarded a bit of cash can protect themselves against “liquidity risk” and can purchase competitor assets at significant discounts. Warren Buffett’s Berkshire Hathaway serves as a wonderful example in this case. Its history and policy of not paying a dividend has drawn naysayers in the past. Yet, this has provided a strategy that positioned the firm to have cash when it is most needed. It has allowed Warren Buffett to follow a strategy of long-term value to investors. The implicit risk decision was tied to strategy. The risk decision and strategy decision go hand in hand.

It is fair to admit that the current economic situation has altered many assumptions about business and markets, and we have seen a massive encroachment (oops, I meant investment) by governments in corporations. This will surely bring new risks to corporations and governments alike. Governments and corporations have different strategies and goals. Although we can more or less agree that corporations are driven to return profits to investors, the role of governments as major shareholders in banks, mortgage-holding firms, automobile manufacturers, and insurance firms is less clear. In part, the governments of the world have provided a rescue plan to stabilize (hopefully) our markets. But such investments by the government come with a price tag. We have already seen the US Congress and UK Parliament adjust and limit banks’ pricing on credit cards. Banks in both countries are furthermore restricted in taking action on defaulting mortgages, as part of accepting the government funds. So, the risks accepted change as the corporate strategy changes. Governments and politicians seem much more sensitive to reputations and public outcries than corporations, suggesting that firms accepting government assistance will likely be addressing a new list of risks and responding to a growing group of constituents. The risk of regulation is high for many industries, and firms should adjust their corporate strategies accordingly.

In driving corporate strategy, we see that risk management is much more than a set of best practices and transferring of risk. Instead, it involves clear identification of those risk accepted. Factors that are believed to drive risk and the data that is predictive of risk should be openly communicated, but this is not limited to risks internal to the firm. Let’s not forget, “Profit is reward for taking risk,” as so wisely put by the famous economist Frank Knight in 1921. Therefore, firms should not only be selective in which risks to take, but willing to pounce when the opportunity presents itself. This involves tracking the risk position of competitors, in order to understand competitive advantages. So, risk management is not an exercise in paranoia, but rather a thoughtful approach to understanding uncertainty, exposures, opportunities, and limits in order to make educated investments. It requires executive involvement, an emphasis on making data-driven decisions, open communication about risks, and a discipline to think through scenarios and ready responses. Indeed, a great many of the winners coming out of our current economic environment will be those that not only held a bit more cash, but had a bit more information than their competitors and were able to seize a window of opportunity.

These lessons show that risk management is really about the identification of key information and its use in the decision-making process. It is not about guidelines or the execution of conventional mathematical models. It is more important than ever as preparing for the unknown requires having the best information not the industry accepted “best practice.” This all signals that the risk management team belongs on the corporate strategy team, not on the phone with insurance brokers.

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