Money Laundering-The Economic Impact and Its Implications: Channels, Institutional Responses, Ethical Dimensions, and a Critical Assessment of Selected Estimates

Abstract Money laundering undermines the integrity of financial systems by concealing the criminal origin, ownership, or control of assets and enabling the proceeds of crime to enter the legitimate economy. In developing economies, the problem can interact with weak governance, corruption, trade-related vulnerabilities, informal value-transfer systems, capital flight, and constraints on domestic resource mobilisation. This article examines money laundering with a particular focus on USA and Bangladesh. It develops a structured account of the principal channels identified in the source manuscript—trade misinvoicing, informal remittance and hundi networks, misuse of financial incentives, cash and financial-sector transactions, and emerging virtual-asset risks—and evaluates their possible macroeconomic and institutional consequences. The article adopts a qualitative, desk-based research design using documentary analysis of legislation, financial-intelligence publications, international standards, central-bank statistics, and peer-reviewed research. It also audits numerical illustrations in the original manuscript. The audit finds that several calculations cannot support the causal conclusions attributed to them: illicit funds cannot simply be added to official foreign-exchange reserves; an exchange rate cannot be derived by dividing reserves into a hypothetical combined amount; trade-value discrepancies are risk indicators rather than proof of laundering; and the original US interest calculation assumes an unsupported constant rate and simple-interest convention. Official Bangladesh Bank data show that reserve figures vary according to gross and IMF BPM6 definitions, making consistent measurement essential. Bangladesh Financial Intelligence Unit reporting demonstrates active suspicious-transaction intelligence activity, including 17,345 STR/SAR reports received in FY2023–24, but report counts should not be interpreted as proven criminal cases or total laundering volumes. The article concludes that effective prevention requires risk-based supervision, trade-data analytics, beneficial-ownership transparency, stronger inter-agency cooperation, proportionate enforcement, and improved asset recovery. It argues that economic estimates should be presented transparently, with explicit assumptions and uncertainty, rather than as definitive measures where direct observation is unavailable.