چکیده مقاله
Money laundering poses a significant threat to the integrity of financial systems, enabling criminal activities by disguising illicit funds as legitimate transactions Detecting these schemes within banking networks is challenging due to their complex, dynamic, and often obfuscated nature Recent advances in graph based analytics offer promising solutions by modeling financial transactions as networks, where nodes represent entities e g , accounts, customers and edges denote monetary flows This paper provides a comprehensive review of graph algorithms applied to uncovering money laundering networks in banking systems We examine key techniques such as community detection, centrality analysis, anomaly detection, and subgraph pattern matching, highlighting their effectiveness in identifying suspicious behaviors Additionally, we discuss real world case studies, computational challenges, and future directions for enhancing detection accuracy using machine learning and scalable graph processing frameworks Our review underscores the potential of graph based approaches in strengthening anti money laundering AML efforts while addressing limitations such as false positives and evolving laundering tactics
کلیدواژهها
نویسندگان
Mahdi Manouchehri
MSc Student in Computer Engineering, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
شیوه ارجاع
Manouchehri، Mahdi،1404،From Transactions to Networks: Leveraging Graph Algorithms for Money Laundering Detection in Banking Systems،دوازدهمین کنفرانس بین المللی مدیریت، حسابداری، بانکداری و اقتصاد ایران،مشهد
ارائهشده در
مجموعه مقالات دوازدهمین کنفرانس بین المللی مدیریت، حسابداری، بانکداری و اقتصاد ایران25 مرداد 1404 · مشهد