An Approach for Unusual Transaction Detection Based on Time Series Modeling and Control Chart
Liu Zhuo-jun · 2013
Detecting suspicious transactions is a vital task for fighting against money laundering. To help anti-money laundering analyst screen customer's unusual transactions and behaviors in massive financial transaction information,we propose a nonlinear stochastic approach based on nonlinear Markov stochastic process,phase space reconstruction and hidden Markov chain for Modeling and fitting financial transaction time series.Then a robust control chart is applied to the estimation of errors from the fitting to detect anomalies.Applying the algorithm to real data examples and simulation,the experiment results suggest that the approach is effective and feasible and can be used for helping the detection of unusual transaction.