FROM CHECKLIST COMPLIANCE TO INTELLIGENCE-LED PREVENTION: A RISK-BASED FRAMEWORK FOR EFFECTIVE ANTI-MONEY LAUNDERING POLICY

Money laundering transforms proceeds of crime into apparently legitimate assets and thereby protects the economic power of criminal organizations, corrupt officials, tax offenders, and other illicit actors. Yet anti-money-laundering systems often produce a paradox: regulated institutions generate millions of alerts and reports, while investigative authorities recover only a limited fraction of criminal proceeds. This thesis argues that the central policy problem is not an absolute shortage of compliance activity but a failure to allocate information and enforcement capacity according to risk. It develops an intelligence-led framework in which customer due diligence, beneficial-ownership transparency, transaction monitoring, suspicious-transaction reporting, supervision, investigation, prosecution, and asset recovery operate as an integrated information chain. The analysis distinguishes formal technical compliance from substantive effectiveness and explains why indiscriminate reporting, defensive de-risking, and uniform controls can reduce both financial inclusion and enforcement quality. It further examines emerging risks from virtual assets, digital payment systems, professional enablers, trade-based money laundering, and cross-border corporate structures. The thesis concludes that effective AML policy requires proportional controls, verified ownership information, interoperable databases, feedback from financial-intelligence units, specialized investigative capacity, and outcome-based evaluation. The objective should not be to maximize the number of reports filed, but to increase the probability that high-risk illicit flows are detected, disrupted, confiscated, and prevented without unnecessarily excluding legitimate users from the formal financial system.