
The illegal wildlife trade (IWT) is a US$20 billion global industry, with significant environmental, economic, and sociopolitical harms. Fueled by a complex interplay of drivers and enabling factors, IWT poses concrete stumbling blocks for several UN Sustainable Development Goals. There is an urgent need for comprehensive reforms to address its multifaceted consequences around the world. In Southeast Asia, IWT is rampant as the region serves as a global hub for this black market. However, because of its characterization as an invisible threat, many aspects of IWT in the region remains understudied, particularly its relationship with illicit financial flows. In this paper, I look at the different economic and legal interventions to address IWT, focusing on regulatory finance. Adapting legal mechanisms from comparative jurisdictions, I propose three measures to be spearheaded by the Association of Southeast Asian Nations (ASEAN) Economic Community: first, to strengthen the implementation of existing financial crime instruments through specialized training and the utilization of emerging technology such as AI and blockchain; second, to append wildlife crimes as predicate offences to financial felonies, and; third, to utilize financial regulation instruments to fight poverty and support inclusive community development, encouraging communities to be proactive partners in long-term wildlife conservation.
Artificial Intelligence, Regulatory Finance, Sustainable Development Goals, Southeast Asia, Illegal Wildlife Trade
Artificial Intelligence, Regulatory Finance, Sustainable Development Goals, Southeast Asia, Illegal Wildlife Trade
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
