Suspicious Financial Transaction Detection Based on Empirical Mode Decomposition Method

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 analyzing prototype by comparing an account time series transaction data against its peer group. It has deeply considered the influence of normal fluctuation widely existing in real life and could efficiently reduce the number of false positives with a better understanding of customers 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 decomposition (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 annual. Then a linear segment approximation method based on hierarchical piecewise linear representation (LPR) is used to fulfil the quick matching of the major tendency parts between two time series. The results from experiments on real life bank data (foreign exchange transaction data sets) exhibit that the EMD can become a vital technique for the analysis of financial suspicious transaction detection