Category: Risk Training

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 russellwalkerphd.com

References:

[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, IDVSolutions.com. 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.

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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 russellwalkerphd.com

Risk Management Leadership Lessons – The Importance of Focusing on Operational Risk

Risk Management Leadership Lessons – The Importance of Focusing on Operational Risk

The Volkswagen case shows us a contemporary case of what can go dramatically wrong when an enterprise does not focus on its operational risk. Worse, it shows what happens when a lack of leadership and presence of cheating overtake the virtues and values of the firm. Operational risk is a major concern for many firms and in particular for financial service firms.

It is important for risk leaders to focus on operational risk for many reasons. Let’s examine some reasons:

  1. Operational Risk is not tied to an investment with a direct upside. Unlike credit and market risk, where the downside exposure is known (or mostly known) at the time of investment, and an upside is projected, setting up an operation or taking on a new vendor introduces operational risk of an unknown and unforeseen nature. There is no upside, generally. Therefore, reducing operational risk is a direct monetary benefit to the enterprise. Removing operational risk requires knowing how and why it occurs, in the first place.
  2. Measuring Operational Risk requires acknowledging it. I once met with a CEO at a bank that told me, “We don’t have operational risk.” I remember telling him in response that until you recognize it as operational risk, you will see operational risk only as unexpected costs via project overrides, unexpected credit losses, and even lawsuits from customers. He informed me, “We have lots of that.” It is not about semantics. Operational risk is an error and unless you are looking for errors, it will simply look like your business, process, or systems have deviated from plan. Removing the error will be impossible from the investment decision to operate. If a loan process has missing data (a common operational risk) and the loans under-perform, the decision might be to shutdown the loan business entirely (not to invest) but the correct action is to fix the operational risk and process for collection of data. Not understanding and measuring operational risk will mean that business decisions are sub-optimal. Operational risk management is about removing the errors and making the business investment more precise going forward.
  3. Critical operations introduce the biggest operational risk. As in all industries, the desire to reduce costs and develop new products is with us constantly in financial services. Outsourcing and new business models have also brought new risks as costs have been removed. The pressure to move into new banking products, such as online, mobile, and RFID payments have introduced operational risks too. It is little surprise that Apple Pay experienced a fraud rate of over 6%, which is more than 60 times that of normal credit cards.[1] Today, every bank and insurance executive fears that day they see customer data breached and shared online. A repeat of the Target case is nightmare for any business leader. Storing, accessing, and transmitting critical data are now some of the most critical decisions facing a financial institution.[2]
  4. Operational Risk is at the root of reputational harm and regulatory risk. When asked, a risk leader, CEO, or board member will report that their greatest concern is harm to the reputation and customer.[3] Next, it is a great concern that a regulatory body might target the firm for behaviors (real or implied) and penalize the firm accordingly, often in response to how a customer has been harmed. The way a business operates is tied to how it treats a customer and how it fails in providing the customer what he or she expected or was promised. Customers sue banks and insurers for their practices and enforcement when something goes wrong. That is operational risk. If you want to get a head of reputational harm and regulatory risk, focus on operational risk detection and prevention. Develop a plan to measure, manage, and lead operational risk.

How a firm operates and makes decisions is tied to how it manages internal decision-making processes. The management of such risk falls under Operational Risk Management. Operational Risk and self-inflicted damages are the cause of the greatest reputational harm. Through cases and simulations, 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 russellwalkerphd.com

[1] http://blogs.wsj.com/digits/2015/03/03/fraud-comes-to-apple-pay/

[2] Deloitte, Global Risk Survey of CROs.

[3] Economist Intelligence Unit, survey of CROs.

Risk Management Leadership Lessons – Operations Are Improved when Leaders Welcome Bad News

Risk Management Leadership Lessons – Operations Are Improved when Leaders Welcome Bad News

Risk Management Leadership Lessons – Operations Are Improved when Leaders Welcome Bad News

As the Volkswagen case unravels before our eyes, it plays out a familiar and repeated lesson on dealing with risk. This lesson is that early warning signs were available, but ignored. It appears that Bosch warned VW of the illegal diesel emissions as early as 2007.[1] It is not entirely surprising that VW and its executives ignored the warning. In fact, many of the great risk-driven crises involve firms that ignored early warning signs. Often early warning signs come as disconfirming information – or bad news – information that suggests the prevailing outlook on things is flawed and that a negative outcome is looming.

Let’s look at some other big failures in risk management and how the ignoring of early warning signs played a dangerous role. Evidence shows that BP had many test results, indicating that the critical pressure levels on the doomed Deepwater Horizon well were questionable. Toyota had the benefit of many years of excessively large numbers of customer complaints about accelerators. GM knew of the ignition problems. And, even famously, the NASA leadership team knew of the vulnerability of rubber O-rings in low temperatures (it was below freezing at Cape Canaveral the night before the launch in January, 1986). In all cases, the organizations ignored the information and elected to interpret it in a different manner. Why?

The answer is tied to how we develop our outlooks or hypotheses for the things around us. In these spectacular failures, the organizations and their leaders had early warning signs. Yet the early warning signs were ignored. As humans, we are predisposed to confirmation bias when confronted with new and disconfirming information. That is to say, when we see data that suggests our outlook is wrong, we first interpret the data in a way that still fits our rose-colored outlook. We attempt to discredit the data, the messenger, or the meaning of the data before we question our outlook and theory.

For instance, it results in the following claims: The drivers are the problem with Toyota automobiles, not the accelerators. Inconclusive pressure tests are common in oil well tests, as noted by BP. There is no statistically shown relationship between O-ring failure and temperature, as asserted by NASA before the Challenger explosion. And at VW, our engines are better, in spite of the data and warnings.

Overcoming these challenges is a fundamental one in the management of risk and decision-making. It involves organizational refocus and a diligent examination of disconfirming information or bad news. For the leader, it means opening your personal and professional network to the upward from of disconfirming information. That is not a one-time task, but a change in how you operate and do business.

How a firm operates and makes decisions is tied to how it manages internal decision-making processes. The management of such risk falls under Operational Risk Management. Operational Risk and self-inflicted damages are the cause of the greatest reputational harm. Through cases and simulations, 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.

books together from amazon

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 russellwalkerphd.com

The Value of Trust: Operating for Success

The Value of Trust: Operating for Success

The Value of Trust: Operating for Success

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.

books together from amazon

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 russellwalkerphd.com

The Increasing Importance of Operational Risk in Enterprise Risk Management

The Increasing Importance of Operational Risk in Enterprise Risk Management

Enterprise Risk Management, as a corporate undertaking, has its deepest roots in financial services. Historically, for banks and insurance firms, the focus within enterprise risk has largely been credit and market risk. The Great Recession of 2008 showed us that liquidity risk and the interplay between a firm and capital markets were also important to consider. Now that sufficient time has passed since the Great Recession, we see that credit and market risk were not the sole causes. Indeed, critical operations and processes at many lending institutions failed. Underwriting procedures, loan processing, and the like were subject to little if any confirmation and oversight, leading to larger and higher credit risk positions than anticipated. Operational risk had reared its ugly head. The wave of regulation that has overtaken the financial services industry since then is largely driven by concerns over processes and procedures that caused harm to customers. The impact of processes and policies has never been greater. There are drivers at work to suggest that operational risk is still increasing, and that in particular, firms should be mindful of certain risk drivers, in the context of enterprise risk management, such as Increasingly Complex Operations, Development of New and Untested Products, Automation and Digitization, Increasing Reputational Impact from Operational Risk, New Focus of Regulators on the Treatment of Customers as Victims, and lastly, Cyber Risk. The disturbing and uncomfortable reality is that operational risk is unintended and, in theory, should not happen, if critical processes are well designed. Operational risk is self-inflicted, or if not self-inflicted, it is the result of unexpected errors or mistakes, all proving to be much more costly and dangerous than initially anticipated. Therefore, this leads firms to pay specific focus on operational risk management as part of enterprise risk management.

Full article at:

http://www.ermjournal.org/index.php/erm

Managing Data Breaches and Cyber Risks

Data Breaches Impact Reputations and Customers

A data breach can lead to terrible consequences for you and your customers. In addition to devastating financial losses, the damage to your reputation and brand may be irreversible. Yet, despite the risks, some firms still view cyber crimes as random events. They take a “this will never happen to me” approach. On the contrary, it can happen to you and there are things you can do to prevent it.
For one, know that hackers don’t pull names out of a hat. They target firms for precise reasons. Either you have something they want or they’ve spotted a weakness in your system that makes you vulnerable. Consider TJX. In 2007, the retail giant reported the largest data breach in history. Out from under the company’s nose, cyber criminals made off with more than 45 million credit and debit card numbers. It turned out the crooks had been siphoning data for nearly two years before TJX detected the breach. How did the hackers do it? They intercepted insecure wireless payment information TJX was sending to its credit card authorizers and banks. TJX was using an outmoded WEP encryption instead of the more secure WAP. The company elected to not install the latest encryption technology, figuring the risk of a breach was low. Sounds familiar. It was also at work in the Target and Home Depot cases. You might argue, TJX’s business was retail, not technology. What did its management know about cyber crime? Probably not as much as they do now. But had they taken the risks more seriously, the event likely would never have happened.
Employees present a risk, too
Sometimes cyber criminals get help from employees inside a company. In 2011, an RSA employee retrieved an email from his junk folder and opened it. The email contained a malware that gave cyber thieves a foothold and allowed them to burrow into the company’s network. That one employee’s oversight ended up costing RSA and its parent company EMC $66 million. Other times, employees inside a company become the cyber criminals themselves. Booz Alan Hamilton gave its employee Edward Snowden access to classified information. Snowden, in turn, went against his employer’s client, the US government, by going public with that information. JP Morgan, Barings Bank and Société Générale are examples of other companies that also have experienced employee fraud or data breaches.
Tips for securing your data
We live in a data-driven society. Fortunately, you can do a few things to mitigate loss, and ensure your data is more secure.
1. Pay attention to the tiniest of details – As we rely increasingly on data automation to do our heavy lifting for us, we open ourselves up to the dangers of processing data inappropriately. Cloud storage and file sharing add to that risk. It’s best to take a detailed approach to examining data flows. Small holes easily can turn into flood gates.
2. Partner with best-in-class data firms – TJX lost money not because of a bad business model or even poor customer service. It lost money because of how it transferred credit card data, a task far outside of running a department store. Target, Home Depot, and many more are suffering the same. Be honest about what you do best and don’t be afraid to partner with experts in data risks and management.
3. Know your employees and their actions – A broad universe of tools (social networks, blogs, and intranet postings) is available for monitoring employee behavior. Many firms even deploy keystroke tracking software to comb messages and emails for legal issues. It is important to educate employees on how their actions can impact a company’s overall data security.
4. Customers expect more than the law – Laws exist that set clear direction on how companies need to process financial and health care data. But as more firms allow data sharing with web services and third-party apps, the risks become greater. Management needs to look to customer expectations regarding the treatment of data.