Regulatory structure and incentives for financial crime in digital asset markets: An analysis of expected utility from a legal and economic perspective

The expansion of fintech and digital asset markets has given rise to new forms of financial crime, particularly money laundering through sophisticated and difficult-to-detect digital asset transactions. Despite many countries having developed clear legal frameworks and penalties, money laundering persists. This situation is not due to inadequate legal provisions, but rather reflects the limitations of regulatory measures that have failed to modify the incentive structures of offenders at the decision-making stage. This research presents a novel contribution by performing the first structural analysis of expected utility in the context of digital assets. It integrates Expected Utility and Deterrence Theory with a legal analysis framework, using comparative case studies from Mt. Gox, Terra–LUNA, Terraform Labs, and ZipMEX. The study found that combining services, cross-chain bridges, and DeFi protocols significantly reduces the probability of detection ( p ) and the cost of the offender ( C ) , independent of state-imposed penalties. Therefore, adding legal provisions or penalties cannot alter the calculation of the offender’s expected utility. To be effective, Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) measures must structurally reduce offenders’ incentives by increasing the probability of detection and raising the cost of criminal activity, coupled with robust international cooperation mechanisms.