There are many business benefits to the efficiencies that IT provides and the vast majority of functions have been automated. Today, businesses do more transactions, of greater value, faster than ever before. This intense dependence on technology has also introduced new risks and vulnerabilities that have large consequences. One of the primary missions, therefore, for any modern organization is to manage the inherent risk within this complex infrastructure. The only rational reason for spending money to reduce operational risk is the expectation that the benefits outweigh the costs.
Subjective measures such as risk-tolerance or risk appetite can lead to serious errors of fact, in the form of excessive fear of small risks and neglect of large ones. The stakes are too great for organizations to rely on intuitive judgments that are error-prone. Creating infrastructures that increase resiliency requires methods that provide better guidance regarding the large number of competing choices that reduce risk . Little guidance can be gained from measuring operational risk either on a subjective low/medium/high value-scale or on its economic loss potential.
Money is the appropriate yardstick for measuring operational risk and prioritizing the myriad of options for reducing risk; traditional methods based on Loss Potential, as in a BIA, are fatally flawed. Answering operational risk questions involve making a number of tradeoffs that go far beyond simple awareness, intuitive judgment, and best practices. The correct economic metric is Expected Loss. Expected loss ensures more effective priority setting and proper resource allocation.
While this might seem like a difficult and complex process, it really is fairly straightforward. The first step is to build a Quantitative Operational Risk Model (QORM) that organizes the loss potential, threat and vulnerability data and makes the detailed and “what if” calculations. This process allows us to collect, as well as, fine-tune the data in a timely fashion. using the appropriate threat information.
The second step is to build an economic database of loss potential. This involves interviews with several internal and external sources. Once the data has been collected and entered into QORM, there is an initial verification step which validates the data and provides for any necessary adjustments to the risk model. After completing any needed adjustments, QORM calculates the Annualized Loss Expectancy (ALE) and single occurrence loss (SOL) calculations and the detail analysis begins with the information provided.
QORM makes the estimating process easy, economical, and supports risk management decision-making by helping managers to develop the information needed to make more informed decisions regarding investments necessary to reduce operational risk and increase business resiliency
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