Abstract
The traditional network theory is unsuitable for money laundering path analysis because of the complexity of financial transaction. The abnormal accounts are identified only by effectively mining the characteristics of fund flow. The importance of transaction node in the whole illegal capital flow is equivalent to that of the connection among these nodes. Through combining the page rank algorithm with the characteristics of fund flow, an improved weighted and iterative initial value mechanism is designed to calculate the transaction heat value of the account, thus the abnormal capital transaction account is screened out. When the method is applied to a money laundering case, the results show the improved page rank algorithm can effectively screen out the key account nodes in the money laundering network and provide clues for the monitoring of money laundering crimes.
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