RISK MANAGEMENT AND MODELING

Measure what Matters, Make better Decisions

Enterprise risk management is a way organizations can identify, measure, assess, and mitigate risk. Unfortunately, the way risk management is performed today usually involves outdated, unscientific processes that are no better – and often worse – than gut feel. Hubbard Decision Research uses a proven risk management methodology that delivers an objective, scientific way to measure and valuate risk to help decision-makers make better decisions.

Risk is everywhere. Every decision that is made by an organization is accompanied by risk at some level. There’s always a potential for a serious event that leads to loss. In the imperfect world in which we live, the actions we want to take are marked by uncertainty – and too much uncertainty can result in anything from decision-making paralysis to bad investments, misallocated resources, and catastrophic events.
 
Risk management processes (RM) are what organizations use to identify their own risk tolerances for a project, the myriad risks and threats associated with the project, and the best ways to reduce risk exposure.
Unfortunately, organizations are held back when it comes to risk assessment because they either have the wrong mindset about risk, use flawed and unscientific methodologies, or fall victim to crippling misconceptions about measurement and quantification that even trip up seasoned statisticians.
 
As stated in Doug Hubbard’s book The Failure of Risk Management, “The research is overwhelmingly conclusive – much of what has been done in risk management, when measured objectively, has added no value to the issue of managing risks. It may actually have made things worse.”
 
To avoid the pitfalls that can lead organizations to making bad decisions – or, even worse at times, no decision at all – Hubbard Decision Research utilizes a unique modeling methodology that offers a better understanding of uncertainty, a better way to quantify and measure risk and other “intangibles,” and more actionable insights to help decision-makers make better decisions about their initiatives, whatever they may be.

the problems with managing risk today

A decision-maker relies on data in some way when he or she makes decisions. Poor decisions often come from poor data – or poor understanding of the data. This is particularly true when it comes to risk.
Managers, risk management consultants, and executives alike often fall victim to misconceptions about RM: what it is, what it can do, and how to do it. These misconceptions include:
  • Believing that risk is an “intangible” and not something that can be measured;
  • Subscribing to methods that aren’t based on statistics or decision science;
  • Thinking that measurements of risk are insufficient because of small sample sizes or not enough data;
  • Depending on subjective judgments (such as with techniques like “weighted scoring”); and
  • Relying too much on experience and instinct when assessing risk instead of quantitative analysis.
Together, these misconceptions lead decision-makers to either pursue a flawed RM method or not use one at all.
 
The implications are profound. Faulty methods have led to bankruptcies, injuries and deaths, catastrophic product launches, and near-collapse of entire financial systems. The unscientific, expert-driven ways organizations have tried to identify threats and risks and figure out how critical they may be – and how to fix the holes – have led to disasters small and large.
 
In short, there are a lot of risks that can damage an organization – and the biggest one of them all is a weak RM approach.

exploring the foundations of effective risk control

Strong enterprise risk management strategies can help decision-makers measure what matters and ultimately make better decisions. It doesn’t completely eliminate risk, but the right approach does help you determine how to deal with the risk that’s present and allocate resources toward the best choice forward.
 
In other words, it helps you be smart about taking chances.
 
A simplified risk management cycle looks something like this:
  1. Identify Risks
  2. Quantify Value of Potential Losses
  3. Quantify Probability of Loss
  4. Determine Risk Tolerance
  5. Create a Tool for Doing the Math
  6. Decide and Act
  7. Measure Results and Evolve
At every stage in the cycle, there is plenty of opportunity for misconception and error. Risk identification for example, may sometimes seem obvious – such as a data center that doesn’t have a backup – but usually it’s not as simple. The same goes for assessing risks and determining the extent of possible losses. Even identifying how to best mitigate risks is fraught with potential for error if it’s done in an unscientific way.
 
The solution is to use quantitative methods to scientifically measure and analyze every component of risk and potential loss.
 
When we talk about doing something scientifically, we mean using statistics and probabilistic risk models in conjunction with research-backed decision science methodology in an objective, unbiased way that isn’t subject to intuition, opinion, or experience – i.e. the “human element” that often leads organizations astray.
 
One area that needs to be defined is uncertainty. Uncertainty is a major component of risk. But it’s unavoidable; there will always be a degree of uncertainty behind a potential decision or action.

By understanding and measuring uncertainty, an organization can gain valuable data that can inform decisions, which can then allow decision-makers to:

 

  • Decide whether to invest in getting more and often times different data;
  • Evaluate the likelihood that an event may happen or loss may occur;
  • Look for cost-effective ways to mitigate those risks; or
  • Assess if it makes sense to proceed with a decision given your stated risk tolerances and level of uncertainty.
A scientific process removes or mitigates human bias. The right strategy can do something most organizations struggle with: proving that their risk management efforts actually work.

Using a proven rm method: applied information economics

The failure of RM is that organizations are left vulnerable to threats because they have not adopted a rigorous, scientific, and objective probability-based strategy based on decision science to measure and assess risks and ways to mitigate said risks.
 
Applied Information Economics (AIE) is a framework combining economics, actuarial science, and other mathematical methods to deliver better risk analysis. Using AIE, Hubbard Decision Research can define the decisions being considered, model what we know now, measure what actually matters, and then use those measurements and analysis to help decision-makers make better decisions.
 
The advantage of using AIE over many of the more popular risk methodologies is that it works. In fact, we measure just how much better it works compared to other methods which are often no better – and sometimes even worse – than risk management by gut feel and intuition.
 
With AIE, an organization can put a number to risk and uncertainty – including the probability that internal and external risks will occur, the likelihood of losses from those risks, and the value of more information. It’s more than mere quantification, however; it’s quantification based on verifiable science.
 
Hubbard Decision Research has served as risk consultants to organizations ranging from Fortune 10 corporations to government agencies, the military, financial institutions, insurance companies, and a wide range of stakeholders and industries. We have helped decision-makers at all levels – from chief risk officers, chief information security officers, CEOs, and other C-suite officers on down to risk managers and other key personnel – make better decisions based on solid, actionable analysis and processes. We have provided training and consultation, created fully-functioning and comprehensive risk avoidance models, and developed the scientific foundations for a risk management program.
The result is more confidence, less exposure to risk, and better firm performance through a statistically-validated decision-making process and risk mitigation plan.
 
Risk is everywhere, and an organization can’t rely on outdated, unscientific, and biased methodologies to avoid a loss event. Contact Hubbard Decision Research to begin a conversation about how your organization can better manage and mitigate risk by measuring what matters – and making better decisions.

Contact HDR today to set up your consulting and quantitative risk assessment.

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