Financial technology innovation impact on trade-based money laundering from environmental crimes: a gravity model approach

This study contributes two significant ways to the literature on trade-based money laundering (TBML). Firstly, we examine TBML dynamics using trade misinvoicing as a proxy, analysing trade data for wood imports and exports between Southeast Asia and various partner economies from 2018 to 2022. Following a gravity approach, our model incorporates economic mass and distance variables. Additionally, we include financial technology innovation (fintech) as a potential influencing factor, given its increasing prevalence. Secondly, we present an empirical TBML model that accounts for multiple destinations with Southeast Asia, a region rich in environmental resources, as the origin. Using Poisson Pseudo Maximum Likelihood (PPML) as the primary estimator, this study reveals that economic mass, distance (a proxy for trade cost), and fintech innovation significantly impact TBML. Fintech's effects on TBML vary between origin and destination countries. Mobile payments increase TBML in origins but decrease it in destinations. Regulatory sandboxes show contrasting effects, reducing TBML in origins while increasing it in destinations, suggesting varied regulatory effectiveness across jurisdictions

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