Transactions on Case-Based Reasoning for Multimedia Data
(ISSN:1867-366X)
Volume 1 - Number 1 - October 2008 - Pages 37-46
Procurement Fraud Discovery using Similarity Measure Learning
S. Rüping, N. Punko, B. Günter and H. Grosskreutz
Fraunhofer IAIS, Schloss Birlinghoven, 53754 St. Augustin, Germany
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.
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