Fraud detection and the false positive problem

  • Improve the predictive model with one that yields a lower number of false positives AND a lower number of false negatives. In other words, improve the precision AND the recall.
  • Automate the handling of at least some of the alerts to minimize the number of analysts needed to handle.
  • Provide better tools to the analysts to increase their accuracy and productivity
  • Compute a danger score for the alerts in order to better prioritize, handle and route alerts. For example, an alert with a high danger score might be routed to a senior analyst versus one with a low score that can be routed to entry-level analysts.
  • It should be intuitive
  • Once a determination is made, the analyst should be able to quickly mark an alert as valid or invalid with a minimum number of clicks and keystrokes.
  • All the information required to make decisions should be easily accessible by the analyst, preferably in one screen.
  • It can make use of your customers as an extension of your analyst staff. No one knows better than your customer if their card was used for nefarious purposes or not. As long as we don’t have too many false negatives, customers usually appreciate and don’t mind a text or a call asking them if a transaction is valid and it the case of true positives are quite grateful that you caught the bad guys before a fraudulent transaction occurred.
  • Clustering in a predetermined number of cohorts using unsupervised learning techniques such as k-means clustering.
  • A more fine-grained classification where only possible values are not just black or white, but shades of gray are permissible and the darker the color, the higher the scrutiny the alert will be given.
  • Use a numeric result on a continuous scale and bounded scale (for example, from 0 to 1000). We can think of this result as a “danger” score. For higher scores, a more rigorous process will be applied to determine if the transaction should be disallowed.

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Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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Alberto Artasanchez

Alberto Artasanchez

Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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