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Research of Identifying Money-Laundering Transactional Behavior Based on RBF Neural Network

Author: JiNa
Tutor: LvLinTao
School: Xi'an University of Technology
Course: Applied Computer Technology
Keywords: Money Laundering Data Mining Anti-money Laundering Radial Basis Function
CLC: TP183
Type: Master's thesis
Year: 2009
Downloads: 34
Quote: 0
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Abstract


At present, money laundering activity has increasingly become a public hazard to the international community. Money laundering refers to such behaviour that deals with criminal proceeds which through transaction, transfer or conversion to be legalized in order to avoid legal sanctions. It will must encourage smuggling, drug trafficking, corruption and terrorist activities. It threats to global economic development and national security seriously. Establishing anti-money laundering mechanisms and introducing new scientific and technological means as soon as possible, increasing the intensity to fight against financial money-laundering criminals has become an increasingly important and urgent task.The rapid development of information technology enabled explosive growth in financial data, comparing the ways of money-laundering which is hidden, professional and innovative as well as the uncertainty and variability of money-laundering behavior, the present passive and static monitoring technology means of anti-money laundering is still lag behind and the intensity of fighting against financial crime is in the stage of human intervention. How to analyze the mass attributes of mixed types or even semi-structured financial data, how to find meaningful behaviors from a large number of financial transaction records, how to analyze these actions to determine whether they have the characteristics of money laundering, then analyze automatedly and process effectively of the accumulated data which need to be addressed urgently in all areas of financial industry during the process of information, this has promoted the formation of new discipline and technique of data mining.With the clear increasing harm of money laundering act done to the society, the awareness level of anti-money laundering is generally improved in governments and international community, these governments and international community are becoming increasingly aware of the importance, complexity, urgency and long-term of anti-money laundering. Anti-money laundering is aiming at variety ways of money laundering to design appropriate monitoring methods, finding out suspicious transaction timely, then give to the anti-money laundering enforcement to do further investigation.This study sought to absorb and develop the domestic and foreign acts about the latest research results of suspicious money laundering transaction identification, aiming at the shortcomings of traditional rule-based or guidance knowledge discovery which are lack of objectivity and a high dependence on specific areas and more difficult to obtain new knowledge model. In this paper, to explore the scientific and effective transaction reporting standard extraction methods of large amounts and information discovery and identification methods of suspicious transaction as the goal.Based on the analysis and comparison of a variety suspicious money laundering transaction recognition algorithms, using radial basis function neural network which are not easy to circumvent and with standard changed dynamically to carry out research of identifying suspicious financial transactions. proposing effective anti-money laundering monitoring model based on radial basis function neural network and realized it, designing anti-money laundering detection simulated system based on RBFNN, then assisted financial institutions in order to conduct real-time monitoring and prediction, thus promoting the automated research and development of anti-money laundering.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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