Risk Modeling Software

Today there is a heightened appetite for risk management due to global economic circumstances. But risk management has always been an intrinsic aspect of business to a higher or lesser degree. However, in the current technology-led business environment, the use of software to effectively manage risk makes logical sense. It provides a level of sophistication that the traditional processes simply cannot offer. Let me explain why.

Risk management essentially involves three stages – identification, quantification, and the on-going management of risks. In reality, these stages are not completely distinct from each other, with each stage influencing and informing the others. For example, an initial quantification of risks may lead to the conclusion that some of the identified risks are in fact not serious enough to warrant further consideration, or that the original description of the risk was not sufficiently precise for meaningful risk management measures to be put in place.

Each of these stages can benefit from the use of supporting risk modeling software. For instance, Microsoft Excel can be used to create a risk register, i.e. a database that records the risks identified, the assessment of the likelihood and impact of each of these risks, the mitigating actions that have been planned, and the assignment of responsibilities for these actions. However, there are many other software tools available, each designed for a specific purpose and focus. To illustrate, enterprise-wide risk management software focuses on the creation of integrated and holistic risk management systems, whereas Monte Carlo simulation and decision tree software place their emphasis on enhancing the quantitative analysis of risks.

The selection of the appropriate risk analysis software should involve very careful thought. The right decision can lead to a very effective implementation, whereas the wrong decision may result in a large amount of wasted investment.

There are some key considerations to bear in mind when selecting the risk modeling software. Choosing software based on how many staff will genuinely be required for the day-to-day risk management process is crucial. It is easy to select software based on the ideal situation that there will be a wide staff involvement in the risk management process. In reality, this may not be possible, potentially resulting in a cumbersome and inflexible solution being chosen over a more stand-alone and flexible application.

Similarly, knowing the level of risk quantification required is important. In fact, best practice risk management now involves the use of quantitative techniques, often using Monte Carlo simulation. When correctly conducted, the process of quantifying risks is rigorous and structured, can expose hidden or biased assumptions, as well as provide a more solid rationale upon which to base the major decisions.

Finally, determining the extent of on-going risk management needed for your business can assist with software selection. 

Needless to say, any software application will be most successful when used by appropriately trained and motivated staff, and when used as a supporting tool within an overall risk management process. Software is not a replacement for process.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

New measures were published by the UK Government in November 2009 to protect communities at risk from ‘flash’ flooding when drains get overwhelmed by sudden downpours.  The Flood & Water Management Bill going before Parliament will give new powers to local authorities to manage surface water.  Environment Secretary Hilary Benn said, “We’ll see more severe weather as climate change takes hold, with heavier rainfall and potential flooding. The weather in the last couple of weeks has shown that this risk is very real.” 

Recent events in Cumbria in the UK suggest climate change really should be at the top of the agenda, and this new bill will certainly go some way to managing the risk associated with it.

Risk analysis has its place in determining the risk associated with climate change, and Palisade's risk modeling software has helped numerous organizations develop reports on likely outcomes. Cambridge University’s Judge Business School recently used @RISK to provide key input to the Stern Review on the Economics of Climate Change. Released in 2006, this report undertaken by the British government by Lord Stern discusses the effect of climate change and global warming on the world economy and is the largest and most widely referenced report of its kind.

They researched issues such as the impacts of the sea level rising and increases in temperature making land infertile or unfarmable, and balanced these against the costs of various options available to tackle global warming.  

At one end of the scale, doing nothing costs nothing, but the environmental consequences will be high. However, activity that reduces the severity of the impacts may itself be very expensive. The aim of the @RISK model was to enable people to make informed decisions on the optimum way to deal with climate change (ie how much to cut back on damaging activity and what methods to use).

@RISK risk simulation software also helped researchers tackle a key problem associated with investigating climate change, namely that the different effects of the various factors which influence it are themselves, undetermined. For example, the historical evidence does not pin down exactly how much global temperatures will increase if CO2 emissions double. @RISK enabled researchers to quantify this uncertainty in order that they have a measurement of the accuracy of their findings.

Risk analysis models should go some way to determining outcomes, and potentially help us prepare for the tragic events that have unfolded in recent days in the UK.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

In a report issued last month, KPMG emphasizes the need for comprehensive, strategic risk management across an organization. Entitled “The Business Case for a Risk Executive: Leading Efforts to Avoid Surprises, Maneuver through Challenges, and Add Value,” the report notes that most current risk management efforts are specific to particular departments, projects, or regulations, and do not approach risk from an enterprise level. This had led to critical oversights and missed opportunities.

To address this gap, KMPG recommends the appointment of a risk executive. This person’s dedicated purpose is “to help prepare the organization to respond to change and the risks that emerge in changing times, and to turn those efforts into opportunities that benefit the organization.” More specifically, such an executive would unify risk approaches across business units and departments, standardize reporting, and establish a common risk “language.” (Note: Risk modeling software and Monte Carlo techniques play central roles in this effort.)

Expounding on the importance of risk management experts, the report concludes, "Without a risk executive, risk management efforts will likely continue to lag and hamper the organization’s effort to recover. But with a risk executive owning the process, risk management can move beyond a support role and help enable the organization to realize its strategic goals and rebuild business value."

» Read the full report (PDF)

@RISK allows you to use historical data to fit data to a probability distribution. The process is very simple: first select the range where the data is located, and then select the Distribution Fitting button. @RISK will guide through the fitting process where you can select a variety of statistical tests such as Chi-Square, Anderson-Darling, Kolmogorov Smirnov, and the Root-Mean Squared Error. View a short tutorial about Distribution Fitting in risk analysis models below.



While the Distribution Fitting functionally is very useful, in some real life cases we need to fit hundreds of distributions, or create filters for certain date ranges or conditions. If we are to do this manually, the fitting process can be overwhelming. A batch fitting function streamlines the process in your risk modeling software.

Palisade’s Custom Development Team  has been helping many of our customers automate this process using the @RISK Developers Kit. With a custom batch fitting add-in, we are able to extract information from external databases and organize data so that the fitting process can be done automatically. The resulting distributions can be dropped with ease into risk analysis models.

If you are interested in this type of consulting support for risk analysis models, please let us know. Feel free to contact your Palisade sales representative.





>> View @RISK tutorials

Javier Ordóñez, Ph.D
Director of Custom Solutions