反洗钱交易监控现代化的战略价值

Anti-Money Laundering ModernizingAML From Reactive Compliance to At a time when financial crime is growing in scale andsophistication, traditional rule-based Anti-Money Laundering(AML) Transaction Monitoring (TM) programs are no longersufficient. Despite significant investments, financial institutions This research explores how financial institutions can modernizetheir AML TM systems to meet the demands of today’s complexfinancial landscape. Drawing on insights from a 2025 survey of50 Tier 1 banks across global markets, the report outlines a clear The report introduces a four-stage maturity model for AML TMmodernization and highlights the role of advanced technologiessuch as AI, machine learning, robotic process automation, andgraph analytics. It also addresses the operational, data, and At Capgemini, we believe that the future of AML lies inintelligent, integrated, and explainable systems that not onlydetect financial crime but also adapt in real time to emergingthreats. With the right strategy, technology, and partnerships, The Path Forward Building Future-Ready AMLCompliance Frameworks The fight against financial crime is entering a new era—one thatdemands agility, intelligence, and collaboration. As this reportoutlines, legacy rule-based AML Transaction Monitoring programsare no longer sufficient to detect and deter increasingly This research identifies the key challenges and opportunitiesshaping this transformation — from data fragmentation andoperational inefficiencies to the promise of agentic AI, graphanalytics, and real-time monitoring. The six-step roadmap At Capgemini, we believe that the future of AML lies inintelligent orchestration—where technology, data, and humanexpertise converge to create resilient, proactive complianceecosystems. Institutions that act boldly today—investing in Let’s modernize AML together—intelligently, December 2025 Modernizing Anti-moneyLaundering (AML) Transaction Contents 03Introduction 04TM systems’ origins and challenges 05Hidden compliance costs:from fragmentation to frustration 10Roadblocks to scalable AI/ML in AML TMand their solutions 15Conclusion and the way forward Introduction modernizing compliance processes.The research draws insights from a 2025survey of 50 Tier-1 banks and financialinstitutions across North America, Europe,the UK, APAC, and Australia. The The global financial services industry isfacing increasing challenges in combatingmoney laundering and financial crimes.Despite significant investments incompliance frameworks, financial crimecontinues to rise, threatening financial participating institutions represent a balanced mix of global investment,commercial, and retail banks. NorthAmerican and European participants arelargely characterized by their strong institutions’ integrity, eroding customertrust, and destabilizing the globaleconomy. According to the United Nations Recent high-profile penalties, such as theUS$3 billion fine imposed on a NorthAmerican bank for its AML failures,highlight the importance of effective TM.These instances reveal the vulnerabilitiesin current AML systems, which rely on The report offersa comprehensiveroadmap for financial institutionstransitioning from traditional, rule-basedsystems to advanced AI-/ML-driven outdated, rule-based approaches thatcannot keep pace with increasingly Strategic priorities for AML TM in 2025Rising operational compliance costsTechnologies that institutions are This report will equip financial institutions,compliance teams, and technologyproviders with insights to navigate the This Viewpoint examines the ongoingtransformation of AML TM systems,focusing on the role of AI and ML in TM systems’ origins and TM originated in the early 2000s as a regulatory imperative to comprehensive AML laws,such as the USA PATRIOT Act. Financial institutions implemented rule-based systemsas a direct response to meet regulatory requirements. These systems rely on predefinedscenarios and thresholds to identify potentially suspicious activities. Their simplicity and Factors leading to rule-based scenarios’ rapid adoption: financial crimes. They create operational inefficiencies, compliance gaps, and customerdissatisfaction, making them inadequate for today’s complex financial landscape. High false positives:These systems overwhelm compliance teams withnon-actionable alerts, diverting resources from addressing real risks Lack of adaptability:Crude detection methods fail to provide nuanced insights into evolving laundering techniques, such as cryptocurrency layering and trade-basedmoney laundering Fragmented customer profiles:Disconnected data across retail, institutionalbanking, and Payment System Providers (PSPs) creates blind spots in monitoring Complex rule management:Several embedded rules make systems hard to Banks must move beyond rule-based reactive compliance and adopt AI-/ML-driventransaction monitoring solutions to improve efficiency, accuracy, and regulator