اراﺋﻪ ﯾﮏ ﻣﺘﺪوﻟﻮژي ﻣﺒﺘﻨﯽ ﺑﺮ ﻧﻘﺸﻪﻫﺎي ﺧﻮد ﺳﺎزﻣﺎﻧﺪه و ﺷﺒﮑﻪﻫﺎي ﻋﺼﺒﯽ ﭼﻨﺪﻻﯾﻪ ﺑﺮاي رﺧﺪادﻫﺎي ﻣﺸﮑﻮك ﺑﻪ ﭘﻮل ﺷﻮﯾﯽ در ﺳﻄﺢ ﺷﻌﺐ ﺑﺎﻧﮏﻫﺎ

Presenting a methodology based on the self-organizing maps and multi-layer neural networks for suspected money laundering events at bank branches

ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ اﻫﻤﯿﺖ ﺳﯿﺴﺘﻢ ﻫﺎي ﺑﺎﻧﮑﺪاري و ﺳﻮء اﺳﺘﻔﺎده از اﯾﻦ ﺑﺴﺘﺮ ﺑﺮاي ﻣﻘﺎﺻﺪ ﭘﻮﻟﺸﻮﯾﯽ، ﻧﯿﺎز ﻣﺒﺮم ﺑﻪ ﭘﯿﺎدهﺳﺎزي ﺳﯿﺴﺘﻢ ﻫﺎي ﺿﺪ ﭘﻮﻟﺸﻮﯾﯽ از ﻃﺮف دوﻟﺖ ﻫﺎ و ﻣﻮﺳﺴﺎت ﺳﯿﺎﺳﺖﮔﺬار در اﻣﻮر اﻗﺘﺼﺎدي ﻣﻮرد ﺗﻮﺟﻪ اﺳﺖ. ﻫﻤﭽﻨﯿﻦ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ رﺷﺪ ﺗﺮورﯾﺴﻢ و ﺗﻘﻠﺐ ﻫﺎي ﺳﺎزﻣﺎﻧﺪﻫﯽ ﺷﺪه و از ﻃﺮﻓﯽ ﺗﺼﻮﯾﺐ ﻗﻮاﻧﯿﻦ ﻣﺘﻌﺪد ﻋﻠﯿﻪ اﯾﻦ ﻣﻮارد ﻧﯿﺎز ﺑﻪ اﯾﻦ ﺳﯿﺴﺘﻢ ﻫﺎ در ﺣﺎل اﻓﺰاﯾﺶ اﺳﺖ. از ﺳﻮي دﯾﮕﺮ، ﭘﯿﭽﯿﺪﮔﯽ رﻓﺘﺎر ﻫﺎي ﻣﺸﮑﻮك ﺑﻪ ﭘﻮﻟﺸﻮﯾﯽ ﺑﻪ ﮔﻮﻧﻪ اي اﺳﺖ ﮐﻪ ﺑﺪون اﺑﺰاري ﻫﻮﺷﻤﻨﺪ و داده ﻣﺤﻮر ﻧﻤﯽﺗﻮان در ﮐﺸﻒ ﭘﻮﻟﺸﻮﯾﯽ اﻗﺪام ﻗﺎﺑﻞ ﺗﻮﺟﻬﯽ اﻧﺠﺎم داد. ﻧﮑﺘﻪ ﻣﻬﻢ و ﺷﺎﯾﺪ ﮐﺎرﺑﺮدي در اﯾﺮان ﻧﺰدﯾﮑﯽ اﯾﻦ ﺳﯿﺴﺘﻢ ﻫﺎ ﺑﺎ ﺳﯿﺴﺘﻢ ﻫﺎي ﺿﺪ رﺷﻮه ﺧﻮاري، ﺗﻘﻠﺐ، ﺗﺨﻠﻒ و ﺳﯿﺴﺘﻢ ﻫﺎي ﺑﺎزرﺳﯽ اﺳﺖ ﮐﻪ ﻣﯽ-ﺗﻮاﻧﺪ ﺑﻪ ﻋﻨﻮان اﺑﺰاري ﮐﺎرآﻣﺪ ﺑﺮاي واﺣﺪ ﺑﺎزرﺳﯽ ﺑﺎﻧﮏ ﺗﻠﻘﯽ ﮔﺮدد. در اﯾﻦ ﻣﻘﺎﻟﻪ روﯾﮑﺮدي ﻣﺒﺘﻨﯽ ﺑﺮ آﻧﺎﻟﯿﺰ و ﭘﺮدازش دادهﻫﺎ ﭘﯿﺸﻨﻬﺎد ﻣﯽﺷﻮد. در اﯾﻦ روﯾﮑﺮد ﺑﺎ اﺳﺘﻔﺎده از ﻧﻘﺸﻪﻫﺎي ﺧﻮدﺳﺎزﻣﺎﻧﺪه ﺷﻌﺐ ﺑﺎﻧﮏ ﺑﺮاﺳﺎس رﻓﺘﺎرﻫﺎي ﻣﺸﺎﺑﻪ ﺧﻮﺷﻪﺑﻨﺪي ﻣﯽﺷﻮﻧﺪ ﺳﭙﺲ ﺑﺎ اﺳﺘﻔﺎده از ﯾﮏ ﺷﺎﺧﺺ ﺧﻄﯽ ﻓﺮاﯾﻨﺪ ﺑﺮﭼﺴﺐﮔﺬاري ﺷﻌﺐ ﺻﻮرت ﻣﯽﮔﯿﺮد. در ﻣﺮﺣﻠﻪ ﺑﻌﺪ ﺑﺎ اﺳﺘﻔﺎده از آﻣﻮزش ﯾﮏ ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﭼﻨﺪ ﻻﯾﻪ، اﻟﮕﻮﯾﯽ ﺟﻬﺖ ﺷﻨﺎﺳﺎﯾﯽ ﺷﻌﺐ ﺑﺎﻧﮏ ﮐﻪ در آﻧﻬﺎ ﻓﺮاﯾﻨﺪﻫﺎي ﻣﺸﮑﻮك ﭘﻮﻟﺸﻮﯾﯽ ﺻﻮرت ﻣﯽ ﮔﯿﺮد ﻣﻌﺮﻓﯽ ﻣﯽﺷﻮد.

Given the importance of banking systems and the misuse of this platform for money laundering purposes, the urgent need for the implementation of anti-money laundering systems by governments and policy makers in economic affairs is important. Also, due to the growth of terrorism and organized fraud, and the passage of numerous laws against these cases, the need for these systems is increasing. On the other hand, the complexity of money laundering suspicious behaviors is such that no significant action can be taken to detect money laundering without intelligent and data-driven tools. An important and perhaps practical point in Iran is the proximity of these systems to anti-bribery, fraud, violation and inspection systems, which can be considered as an efficient tool for the bank's inspection unit. This paper presents an approach based on data analysis and processing. In this approach, using self-organizing maps, bank branches are clustered based on similar behaviors, then the process of labeling branches is performed using a linear index. In the next step, using the training of a multi-layer neural network, a model for identifying bank branches in which suspicious money laundering processes take place is introduced.