The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge in the field is to identify associated links between suspicious customers who are involved in money laundering. To consider this challenge, this paper discusses the challenges associated with identifying relationships such as business and family relationships and proposes a model to identify links between suspicious customers using social network analysis (SNA). The proposed model aims to identify various mafias and groups involved in money laundering activities, thereby aiding in preventing money laundering activities and potential terrorist financing. The proposed model is based on relational data of customer profiles and social networking functions metrics to identify suspicious customers and transactions. A series of experiments are conducted with financial data, and the results of these experiments show promising results for financial institutions who can gain real benefits from the proposed model.

任何国家的经济和政治体系的稳定性在很大程度上取决于反洗钱政策。如果政府政策无法以适当方式处理洗钱活动，则可以将经济控制权转移给罪犯。当前的文献提供了各种技术解决方案，例如基于聚类的异常检测技术，基于规则的系统和决策树算法，以控制可以帮助识别可疑客户或交易的活动。但是，文献没有提供有效和适当的解决方案来帮助识别可疑客户或交易之间的关系。该领域当前的挑战是确定参与洗钱活动的可疑客户之间的关联链接。为了考虑这一挑战，本文讨论了与识别诸如企业和家庭关系等关系相关的挑战，并提出了一种使用社交网络分析（SNA）识别可疑客户之间联系的模型。拟议的模型旨在确定参与洗钱活动的各种黑手党和团体，从而有助于防止洗钱活动和潜在的恐怖分子筹资活动。所提出的模型基于客户档案的关系数据和社交网络功能指标来识别可疑客户和交易。对金融数据进行了一系列实验，这些实验的结果表明，对于可以从所提出的模型中获得实际收益的金融机构而言，这些结果令人鼓舞。本文讨论了与识别诸如企业和家庭关系等关系相关的挑战，并提出了一种使用社交网络分析（SNA）识别可疑客户之间联系的模型。拟议的模型旨在确定参与洗钱活动的各种黑手党和团体，从而有助于防止洗钱活动和潜在的恐怖分子筹资活动。所提出的模型基于客户档案的关系数据和社交网络功能指标来识别可疑客户和交易。对金融数据进行了一系列实验，这些实验的结果表明，对于可以从所提出的模型中获得实际收益的金融机构而言，这些结果令人鼓舞。本文讨论了与识别诸如企业和家庭关系等关系相关的挑战，并提出了一种使用社交网络分析（SNA）识别可疑客户之间联系的模型。拟议的模型旨在确定参与洗钱活动的各种黑手党和团体，从而有助于防止洗钱活动和潜在的恐怖分子筹资活动。所提出的模型基于客户档案的关系数据和社交网络功能指标来识别可疑客户和交易。对金融数据进行了一系列实验，这些实验的结果表明，对于可以从所提出的模型中获得实际收益的金融机构而言，这些结果令人鼓舞。