Suspicious Financial Transaction Detection Based on Empirical Mode Decomposition Method

• Authors: Tianqing Zhu
• Venue: APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
• Year: 2006

Traditional financial surveillance system usually discriminates suspicious transaction by comparing every transaction against its corresponding account history. This process always results in high false positive rate because it is regardless of the existence of economic cycle and business fluctuation. We conceived a new analyzingprototype by comparing an account time series transaction data against its peer group. It has deeply considered the injuence of normal jluctuation widely existing in real life and could eflciently reduce the number of false positives with a better understanding of customer 5. behavior pattern. A new method Empirical Mode Decomposition (EMD) developed initially for natural and engineering sciences has now been applied to financial time series data. This method has shown its superiorities in analyzing nonlinear and nonstationary stochastic engineering time series over traditional Discrete Fourier Decomposrtlon (DFD) and wavelet decomposition methods. Firstly the complex financial time series is decomposed into some local detail parts and one global tendency part which represent different time scales like daily, monthly, seasonal or annuat. Then a linear segment approximation method based on hierarchical piecewise linear representation (LPR) is used to fulfill the quick matching of the major tendency parts between two time series. The results from experiments on real life bank data lfoveign exchange transaction data sets) exhibit that the EMD can become a vital technique for the analysis offinancial suspicious transaction detection.