In a humorous vignette from Quino, a character in an industrial city is suffering from strong coughing fits. The character walks through the city to the health center and along the way we see factories that release clouds of heavy smoke. Walking back to the pharmacy to get the medication prescribed, the reader continues to see the same factories releasing the smoke into the atmosphere, reinforcing the idea of pollution having caused the disease. The character leaves the pharmacy, takes the medication, and his health improves immediately, at least until the character has the same type of attack again. In the last vignette, and in a sweeping manner characteristic of the caustic humor of Quino, the conclusion of the story appears to the reader. All factories in the city were causing pollution and the cause of the disease and produced a single product: cough medication!
What does this have to do with fraud? At first glance, not much. Nevertheless the analogy is perfect as an introduction to TM Forum’s new Fraud Classification Model (GB954) and its innovative approach. By splitting the causes from the “illness” of fraud, the approach supports an effective root cause analysis of fraud. By providing an extensive and well-defined set of attributes to classify fraud cases and record the applied mitigation strategy, it forms the foundation for identifying the best mitigation strategy.
This new model was developed in early 2012 and has been tested thoroughly since then. It classifies the fraud cases under two main topics:
What does this have to do with fraud? At first glance, not much. Nevertheless the analogy is perfect as an introduction to TM Forum’s new Fraud Classification Model (GB954) and its innovative approach. By splitting the causes from the “illness” of fraud, the approach supports an effective root cause analysis of fraud. By providing an extensive and well-defined set of attributes to classify fraud cases and record the applied mitigation strategy, it forms the foundation for identifying the best mitigation strategy.
This new model was developed in early 2012 and has been tested thoroughly since then. It classifies the fraud cases under two main topics:
- Enabler Technique – What vulnerability was explored to access to the network, products or services?
- Fraud Type – What fraud was committed (at the network, products or services level) to gain illegal benefit by exploring the vulnerability above?
In some circumstances, the “Enabler Technique” is not a fraudulent attack but the exploration of a vulnerability that can be later translated into a fraudulent scenario. Flaws in products or services offer design, or in security, are good examples of vulnerabilities. By splitting these and keeping an objective and consistent track record, fraud teams can be much more effective in how to address and prevent fraudulent attacks.
Like in Quino’s cartoon, we will hardly be effective by just treating the symptoms if we don’t clearly understand the illness and what is causing it. The Fraud Classification Model is definitely an important step to continue extending TM Forum best practices in the Fraud Management area, answering a need clearly expressed in TM Forum’s 2012 Fraud Survey conclusions about the nonexistence of a standard classification methodology and the consequent impossibility of analysis and benchmark.
Like in Quino’s cartoon, we will hardly be effective by just treating the symptoms if we don’t clearly understand the illness and what is causing it. The Fraud Classification Model is definitely an important step to continue extending TM Forum best practices in the Fraud Management area, answering a need clearly expressed in TM Forum’s 2012 Fraud Survey conclusions about the nonexistence of a standard classification methodology and the consequent impossibility of analysis and benchmark.
This article was first published in TMForum’s Inside Revenue Management Issue 69. You can find the original version here.
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