Small Town Corruption: The Cautionary Tale of Jasiel Correia

Elected at the age of 23 to serve as mayor of Fall River, Massachusetts, Jasiel Correia looked like a wunderkind. A tech entrepreneur who founded his own startup, Correia was the youngest-ever mayor of his hometown, the golden boy who promised to use his technological prowess and puckish energy to bring his aging town into the 21st century

Then it all came crashing down. In 2018, Correia was charged with various personal misdeeds, including tax and wire fraud, related to his tech company. A defiant Correia maintained his innocence and rejected calls for his resignation. Then, a second round of charges hit, this time alleging public corruption. Correia purportedly took over $600,000 in bribes from marijuana business license applicants—including one marijuana business owner who paid the Mayor $100,000 and promised him 2% of his future sales revenue in exchange for a lucrative operating permit. By the time Mayor Correia went to trial, he faced 24 separate criminal charges, and on May 14, 2021, the jury found him guilty of 21 of those 24 counts.

Mayor Correia’s downfall might seem like a relatively minor matter involving local corruption in one small city. (Such stories are, alas, all too common.) But this incident usefully highlights the corruption risks associated with devolving regulatory authority to local governments. While there are certainly virtues of giving local governments power over local affairs, we need to be clear-eyed about the dangers that local control can pose, particularly in the context of regulating lucrative industries like legal marijuana. The Fall River example highlights several such risks:

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ML for AML: Is Artificial Intelligence Up to the Task of Anti-Money Laundering Compliance?

Fighting corruption—especially grand corruption—requires effective anti-money laundering (AML) systems capable of efficiently and correctly flagging suspicious transactions. The financial institutions responsible for identifying and reporting suspicious transactions employ automated systems that identify transactions that involve certain red flags—characteristics like transaction amount, location, or deviation from a customer’s typical activity; when the automated system flags a transaction, this triggers further review. But—given the ever-increasing volume and complexity of financial transactions that occur each day, as well as the increasing sophistication of kleptocrats, criminal groups, and others in disguising their illicit activities to avoid the usual red flags—picking out the genuinely suspicious transactions can be extraordinarily difficult. Even the cleverest compliance system designer couldn’t hope to incorporate every potential red flag into the automated system.

The need to stay one step ahead of the bad actors has fueled greater interest in how new advances in data processing technology may help make automated suspicious transaction detection systems more effective. Techno-enthusiasts are particularly interested in deploying deep learning artificial intelligence (AI), as well as classic algorithms that fall under the machine learning (ML) umbrella, in the AML context. ML and AI systems extract patterns from training datasets, and “learn” (by induction) what data patterns are associated with particular identifiable categorizations. Email spam filters provide a simple example. A spam filter, which can be created to conduct a process known as classification, sorts input variables into two categories: “spam” and “not spam.” It makes its categorization based on individual characteristics of the emails (such as the sender, body text, etc.). In the AML context, the idea would be to train an algorithm with data on financial transactions, so that the system “learns” to identify suspicious transactions even in cases that might lack the usual red flags that a human designer would program into an automated system. Advocates hope that ML/AI systems could be used both to filter out the false positives (transactions which are flagged as suspicious but turn out, on review, not to raise any concerns—an estimated 99% of all flagged transactions), while also identifying unusual, potentially fraudulent behavior that may be overlooked by human regulators (false negatives). Indeed, industry experts are understandably enthusiastic about AI systems that will cut costs while improving accuracy, and proponents claim that “AI holds the keys to a more efficient and transparent AML stance[,]” urging that “[b]anks must take hold of this new [AML] weapon[.]”

To the extent that AI tools can improve upon the admittedly-clunky automated systems currently in use, it could be a step forward. But ML/AI systems have a less than stellar track record in other contexts, and a model targeted at AML compliance presents some unique challenges.

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