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Despite 2016 legalization of recreational cannabis cultivation and sale in California with the passage of Proposition 64, many cannabis businesses operate without licenses. Furthermore, federal regulations disincentivize financial institutions from banking and lending to licensed cannabis businesses. The authors explore the impact of legal cannabis business activity on California financial institutions, the barriers to banking faced by cannabis businesses, and the nontraditional sources of financing used by the industry.

The authors use a mixed methods approach. The authors utilize call data for banks and credit unions headquartered in California and state cannabis licensing data to estimate the impact of the extensive and intensive margins of licensed cannabis activity on key banking indicators using difference-and-difference and fixed effects regressions. The qualitative data come from interviews with industry stakeholders in northern California's “Emerald Triangle” and add important context.

The quantitative results show economically and statistically significant impacts of licensed cannabis activity on banking indicators, suggesting both direct and spillover effects from cannabis activity to the financial sector. However, cannabis businesses report substantial barriers to accessing basic financial services and credit, leading to nontraditional financing arrangements.

The results suggest opportunities for cannabis businesses and financial institutions if regulations are eased and important avenues for further study.

The authors contribute to the nascent literature on cannabis economics and the literature on banking regulation and nontraditional finance.

This paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.

This paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.

A new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.

This study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

The relationships between tourist resorts and transnational crime are rarely analyzed systematically. This paper begins to fill this gap by examining how organized crime groups and individuals linked to them can take advantage of tourist resorts to commit crimes.

The reality of domestic violence does not disappear when people enter the digital world, as abusers may use technology to stalk, exploit, and control their victims. In this chapter, we discuss three unique types of technological abuse: (1) financial abuse via banking websites and apps; (2) abuse via smart home devices (i.e., “Internet of Things” abuse); and (3) stalking via geo-location or GPS. We also argue pregnancy and wellness apps provide an opportunity for meaningful intervention for pregnant victims of domestic violence.

While there is no way to ensure users' safety in all situations, we argue thoughtful considerations while designing and building digital products can result in meaningful contributions to victims' safety. This chapter concludes with PenzeyMoog's (2020) “Framework for Inclusive Safety,” which is a roadmap for building technology that increases the safety of domestic violence survivors. This framework includes three key points: (1) the importance of educating technologists about domestic violence; (2) the importance of identifying possible abuse situations and designing against them; and (3) identifying user interactions that might signal abuse and offering safe interventions.