Procurement Fraud Discovery using Similarity Measure Learning
Stefan Rüping, Natalja Punko, Björn Günter, Henrik Großkreutz · 2008
Abstract. This paper describes an approach to detect risks of procurement fraud. It was developed within the context of a European Union project on fraud prevention. Procurement fraud is a special kind of fraud that occurs when employees cheat on their own employers by executing or triggering bogus payments. The approach presented here is based on the idea to learn a similarity measure that compares an employee (or payroll) standing-data record to a creditor record, in order to detect creditors that are suspiciously similar to employees. To this ends, it combines several simple similarity measures like address similarity or spatial similarity using a weighting scheme. The weights, that is the overall similarity function, are learned from user input specifying whether a particular pair of payroll and creditor data records are similar. This leads to an adaptive, easily transferable approach for a generic class of fraud opportunities.