Research On Suspicious Transaction Detection Model And Investigation Countermeasure Of Internet Money Laundering Crime

With the rapid development of information network technology in recent years,new forms and models of the Internet economy have not only facilitated people’s work and life but have also provided new space for the development of money laundering crimes.Money laundering criminal groups manipulate online platforms to achieve cross-platform and cross-regional movement of funds,making the flow of funds more complex and significantly enhancing the deceptive nature of transactions,thus posing great challenges to current anti-money laundering efforts.This article is based on the perspective of investigation studies on anti-money laundering efforts.In response to the current investigative challenges posed by internet money laundering crimes,a detection model for suspicious transactions has been developed and applied in practical work.The aim is to advance the investigative work on internet money laundering crimes towards a proactive investigative model.The main research content includes the following four parts:The first part provides an overview and characteristic analysis of internet money laundering crimes.This study refers to existing research outcomes to define the concept of internet money laundering crimes from both broad and narrow perspectives.It also introduces four typical categories of internet money laundering crimes,including cryptocurrency money laundering,third-party payment platform money laundering,P2 P platform money laundering,and online banking money laundering.Regarding the characteristics of internet money laundering crimes,the analysis focuses on aspects that differentiate them from traditional money laundering crimes,including low crime costs,concealed identities of perpetrators,cross-platform transfer of illicit funds,and transnational execution of crimes.The second part focuses on the research of investigative challenges in internet money laundering crimes.This section analyzes the difficulties faced in current investigations of internet money laundering crimes from three aspects: data utilization,clue discovery,and investigation and evidence collection.It is observed that current investigative work has not completely overcome the limitations of the traditional retrospective investigative model and has failed to fully harness the value of data.The third part of the research focuses on the study of suspicious transaction detection models for the internet.Considering the increased deceptive nature of money laundering transactions in current internet crime,it is necessary to explore the interaction information between nodes in the transaction network when constructing the model.Therefore,this paper adopts graph neural networks to learn the relationships and contextual information among transaction nodes.In terms of model construction,an evolutionary graph convolution mechanism is proposed,which utilizes the sequence model Transformer to update the parameters of the graph convolutional network to capture the temporal relationships between transactions.The Focal Loss function is introduced to address the problem of imbalanced sample categories,thereby improving the classification accuracy and recall for money laundering transactions.These methods effectively stabilize network training and enhance the classification performance of the model,especially when dealing with imbalanced transaction data.They hold significant practical value in the identification of suspicious transactions related to internet money laundering.Extensive comparative and ablation experiments were conducted on the elliptic cryptocurrency transaction dataset to demonstrate the practical applicability of the proposed methods in identifying internet money laundering crimes.The fourth part focuses on the research of investigative strategies for internet money laundering crimes under the detection model for suspicious transactions.Based on the investigative challenges of internet money laundering crimes outlined in the previous sections,this study integrates the developed detection model for suspicious transactions into anti-money laundering investigative work and proposes strategies to achieve a proactive investigative model.These strategies include: Building an anti-money laundering big data platform and establishing various foundational databases;Cultivating a mindset of big data investigation and enhancing the capability to utilize data effectively;Improving the detection model for suspicious transactions and constructing an intelligent anti-money laundering investigative system;Applying the detection model for suspicious transactions to guide investigative and evidence collection work.