Extraction of Fraud Schemes from Trade Series

Abstract

It is very often the case that the patterns of a fraudulent activity in trade are hidden within existing trade data time series. Furthermore, with the advent of powerful and affordable computing hardware, relatively big amounts of available trade data can be quickly analyzed with a view to assisting antifraud investigations in this field. In this paper, based on the availability of such import/export data series, we present a statistical method for the identification of potential fraud schemes, by extracting and highlighting those cases which lend themselves to further investigation by anti-fraud domain experts. The proposed method consists in applying time series analysis for prediction purposes, calculating the resulting significant deviations, and finally clustering time series with similar patterns together, thus identifying suspect or abnormal cases.