Predictive Fraud Analytics: B-Tests

29 Pages Posted: 18 Oct 2018

Date Written: October 15, 2018

Abstract

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.

Keywords: B-test, Benford’s law, fraud detection, predictive fraud analytics, fraud modeling.

Suggested Citation

Afanasiev, Sergey and Smirnova, Anastasiya, Predictive Fraud Analytics: B-Tests (October 15, 2018). Journal of Operational Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3268098

Sergey Afanasiev (Contact Author)

Independent

No Address Available

Anastasiya Smirnova

Independent

No Address Available

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