Journal of Operational Risk

Predictive fraud analytics: B-tests

Sergey Afanasiev and Anastasiya Smirnova

This paper considers:

  • B-tests – predictors for bank internal fraud detection, as a generalization of the Benford's law;
  • Metrics of proximity of two distributions, for the calibration of B-tests;
  • Target variable of potential loss, allowing to adjust operational B-tests;
  • Results of B-tests' calibration on a sample of bank loan applications.

In the banking sector, machine-learning methods are applied in a wide variety of business areas: assessing a client’s risk profile (application and behavior scoring), forming targeted sales (x-sell, up-sell), choosing collection strategies (collection scoring), etc. The bank anti-fraud division is no exception, where with the help of machine-learning methods effective anti-fraud tools are developed. This paper deals with B-tests: methods by which it is possible to identify internal fraud among employees and partners of the bank at an early stage.

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