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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President Biden: Fighting Corruption Core U.S. National Security Interest

Last Thursday President Biden officially declared what corruption fighter have long known:

“Corruption corrodes public trust; hobbles effective governance; distorts markets and equitable access to services; undercuts development efforts; contributes to national fragility, extremism, and migration; and provides authoritarian leaders a means to undermine democracies worldwide.  When leaders steal from their nations’ citizens or oligarchs flout the rule of law, economic growth slows, inequality widens, and trust in government plummets.”

Memorandum on Establishing the Fight Against Corruption as a Core United States National Security Interest

Biden then did what no corruption fighter could. He issued a National Security Memorandum making “countering corruption . . . a core United States national security interest.”   To that end he pledged “to promote good governance; bring transparency to the United States and global financial systems; prevent and combat corruption at home and abroad; and make it increasingly difficult for corrupt actors to shield their activities.”

The Biden memo directs the most senior member of his government to develop a presidential strategy to fight corruption both within the United States and abroad that targets precisely the issues the global anticorruption community, including this Blog, have identified as critical. They are measures to: combat illicit financial flows; increase asset recovery efforts and the return of stolen assets to victim states; target grand corruption by leaders of foreign states; strengthen civil society, the media, and other agents of accountability; incorporate anticorruption measures into foreign assistance programs; pressure international agencies and organizations to focus on the demand side of bribery; and enhance U.S. assistance to foreign law enforcement agencies investigating and prosecuting corruption.

That the Biden memo reads like the anticorruption community’s wish list should come as no surprise. Before taking up his post as Biden’s National Security Adviser, Jake Sullivan was a member of the community in good standing (some of his writings on corruption here, here, and here), and in his first interview after being named the president’s top adviser on foreign policy he said his goal was “to rally our allies to combat corruption and kleptocracy, and to hold systems of authoritarian capitalism accountable for greater transparency and participation in a rules-based system.”

The headline on a column on the prospects for success of the Biden initiative by the Washington Post’s leading foreign affairs commentator captures what I suspect are GAB readers’ sentiments: “Biden’s anti-corruption plan appears to have some teeth. Here’s hoping they bite.”

Reforming South Korea’s New Anticorruption Agency: How to Promote Independence without Inducing Paralysis

Back in December 2019, South Korean President Moon Jae-in achieved what seemed like a major victory in his anticorruption platform when the National Assembly established a new agency, the Corruption Investigation Office for High-Ranking Officials (CIO). Armed with broad investigatory authority, as well as a more limited but nonetheless important power to prosecute members of the Supreme Prosecutor’s Office (SPO), the CIO was supposed to be at the vanguard of the effort to clean up South Korean government. Yet for over a year, the CIO was unable to operate because it had no Director General. The reason for this had to do with the original design of the mechanism for selecting this official. In an effort to ensure a consensus candidate and avoid politicization of the agency, the original CIO legislation required that a Director General candidate receive the support of six out of the seven members of a Recommendation Committee composed of the Minister of Justice, the Minister of Court Administration, the President of the Korean Bar Association, two members from President Moon’s party, and two members from the opposition People Power Party (PPP). That system meant that at least one opposition party member would need to support a candidate for that candidate to be appointed, thus preventing the President from installing a crony.

The system, however, did not work as intended, because the two PPP members on the Committee refused to confirm any of the candidates put before the Committee. Finally, in December 2020, a year after the CIO’s creation, the National Assembly passed a bill that reduced the number of votes needed to recommend a candidate from six to five. This enabled the Recommendation Committee to appoint (over the opposition of the Committee’s two PPP members) the CIO’s first Director General, Kim Jin-wook, and the CIO finally began operating in January. Naturally, the PPP was outraged. This change to the appointment procedure, the PPP argued, undermines the CIO’s independence and enables the President to ensure that this powerful agency is run by a loyalist, who is likely to be unfairly biased against the opposition.

This concern is fair, up to a point. Three of the seven members of the Committee—the two members of the majority party and the Minister of Justice—are closely aligned with the President. The Minister of Court Administration is appointed by the Chief Justice of the Supreme Court, not the President, but the President appoints the Chief Justice, and Korean Chief Justices have a history of colluding with presidents. A fifth member, the President of the Korean Bar Association, is elected by a vote among the local bar chapters. While this may provide some check on the President, it is a weak one, and the PPP and other critics are right to be concerned.

Nevertheless, the reduction in the required number of votes from six to five was an improvement under the circumstances. The threat of biased anticorruption investigations, though real, is not much greater with the new version of the CIO than under the status quo. And while greater safeguards would be welcome, there are better ways to promote an unbiased agency than to give the opposition a veto over its leader.

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Improving Anti-Money Laundering Models with Synthetic Data

As readers of this blog are well aware, an effective anti-money laundering (AML) regime is crucial for fighting grand corruption, as well as other organized criminal activity. A key part of the AML system is the requirement that banks and other financial institutions identify suspicious transactions and file so-called suspicious activity reports (SARs) with the appropriate government agencies. This is an enormous task, given the volume of financial transactions that banks need to monitor and the challenge of identifying which of those transactions ought to be considered suspicious. Banks spend billions on AML compliance every year, and have developed complex automated systems to assist them in flagging suspect transactions, but existing systems’ ability to efficiently sort suspicious from innocent transactions is limited by the sheer complexity of the task. (False positive rates with current systems, for example, frequently top 90%.)

Many believe that artificial intelligence (AI) systems, such as those employing machine learning (ML), hold enormous promise for improving AML compliance and reducing cost. ML algorithms scrutinize vast datasets to identify patterns that can be used to fashion predictive models. In the AML context, ML algorithms identify those transaction characteristics (or complex combinations of transaction characteristics) that are associated with money laundering, and use these patterns to more efficiently and effectively identify suspicious transactions.  

But some commentators have suggested reasons for skepticism, or at least caution. For example, Mayze Teitler recently wrote on this blog about a number of challenges to operationalizing AI-derived algorithms in the AML context, primarily those arising from limitations in the data on which those algorithms are based. As Mayze correctly pointed out, ML algorithms require vast datasets from which to learn, and the data demands are compounded by the relatively rarity of known money laundering cases in the existing datasets.

Despite these concerns, I am more bullish than Mayze regarding the promise of AI-based AML systems. Many of the challenges and concerns regarding the development of effective AI systems in the AML context can be overcome through the use of synthetic data.

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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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Peru’s Misguided Proposal for Countering Corruption in Arbitrarion

In Peru, as in far too many countries, the judicial system is corrupt and unreliable. For this reason, companies often find arbitration is an attractive alternative for resolving commercial disputes—not just because arbitration can be cheaper and faster than judicial dispute resolution in these cases, but because the arbitrators are (supposedly) less likely to be corrupt than judges. Alas, corruption has found its way into commercial arbitration in Peru as well, as illustrated most prominently by a recent case in which agents of the Brazilian construction firm Odebrecht allegedly paid bribes to arbitrators to secure favorable decisions in pending cases between Odebrecht and the Peruvian government (see hereherehere and here). 

bill was introduced into the Peruvian Congress this past February that, according to its proponents, would address this problem. This bill would amend Peruvian arbitration law to add a requirement that all international arbitrators hearing domestic cases have their qualifications certified by the state education regulator (known by its Spanish acronym SUNEDU) within 30 days. On its face, this requirement doesn’t seem to have much to do with corruption. But the bill’s advocates have been quite explicit that this new rule should be understood as a way to prevent future corruption of arbitration proceedings in Peru. According to the bill’s supporters, corruption in arbitration arises because foreign arbitrators do not understand Peruvian anticorruption laws; therefore, the argument continues, requiring a state agency to validate the credentials of these foreign arbitrators would ensure that they understand the Peruvian system, including the prohibitions on corruption in the arbitral system and the regulation on corruption more generally (see here and here).

If that sounds silly, it’s because it is. This bill not only fails to address the actual sources of corruption in Peruvian arbitration, but might actually make things worse. Arbitral corruption is a genuine problem in Peru, but this is not the right way to address it.

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Social Damages for Corruption: Examples Please

Faithful readers know that for a StAR/UNODC project I am searching for cases where corruption victims were compensated for their losses.  One area where I desperately need assistance is in locating awards for social damages. 

Recovery for social damage was pioneered by Costa Rican jurists.  Article 38 of the Costa Rican penal code gives the Procuraduría General de la República the power to recover damages for acts that affect diffuse or collective interests. Termed “social damages,” the PGR web site lists five corruption cases where over $41 million in social damages have been collected.  The cases have generated learned commentary both in Costa Rica (examples here and here) and in other Latin American states (here and here). Likely because I read Spanish poorly and slowly, beyond the Costa Rican ones, I can find no case where social damages for corruption have been awarded.  Help from readers with examples or leads on where I might find examples is solicited.

For the uninitiated, social damages are compensation paid to redress harm to the welfare of a community. A community’s welfare is the combination of economic and non-economic conditions that together produce a sense of satisfaction, happiness, health, and so forth. To me, it seems to parallel Amartya Sen’s argument that GDP alone is not a sufficient measure of a nation’s well-being though I have yet to see the link made.

Thanks again to readers who responded to my earlier queries.  As with those, submissions in any language Google Translate reads welcome.

South Korea’s New Corruption Investigation Office Needs Independent Prosecutorial Power

When South Korean President Moon Jae-in took office, it was clear that fighting corruption was going to be high on his agenda. After all, his predecessor Park Geun-hye was sentenced to 24 years for pressuring conglomerates such as Samsung and Lotte to give millions of dollars to her friend’s foundation. And the president before her was sentenced to 15 years for collecting bribes of up to $5.4 million from Samsung in exchange for favors. President Moon capitalized on the nation’s anger and sense of betrayal, pledging to crack down on corruption. Part of his reform agenda included addressing how Korea’s investigative and prosecutorial bodies—including the Supreme Prosecutor’s Office (SPO)—have handled, or mishandled, corruption cases.

This concern led to the enactment, in 2019, of legislation authorizing the creation of a new agency called the Corruption Investigation Office for High Ranking Officials (CIO). The CIO can investigate certain crimes, such as bribery and embezzlement, related to the duties of current and retired high-ranking public officials—including, but not limited to, the President, SPO prosecutors, judges, and members of the National Assembly. The CIO has the authority to investigate current and former officials, their family members, and other individuals who are implicated in the crimes under investigation. This means if a company employee bribes the grandson of a public official, then the CIO can investigate the company. Furthermore, other law enforcement agencies must immediately notify the CIO when they learn of crimes that fall under the CIO’s investigative jurisdiction, and the CIO can compel those cases to be transferred to it.

There is, however, a significant problem with this new system, one that will likely impede the CIO’s ability to hold high-level politicians and their cronies accountable: The CIO lacks the power to prosecute most of the cases it investigates. The CIO does have the limited authority to prosecute SPO prosecutors (including the Prosecutor General, who heads the SPO), as well as judges and high-ranking police officers. But for all of its other investigations, the CIO must turn the results of its inquiries over to the SPO, which retains the discretion to decide whether or whom to prosecute. Without independent prosecutorial authority, the CIO is unlikely to live up to its potential to make significant progress against high-level corruption.

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Why Are the Architects of China’s Anticorruption Campaign (Mis-)Reading Tocqueville?

Back in 2013, senior Chinese Communist Party (CCP) official Wang Qishan, who was then head of the CCP’s Central Commission for Discipline Inspection (CCDI), and is widely considered the architect of President Xi Jinping’s anticorruption campaign, instructed party officials to read Alexis de Tocqueville’s L’Ancien Régime et la Révolution (The Old Regime and the Revolution). This is notable in part because the Xi regime has a reputation for rejecting Western thinking, particularly with respect to governance. The timing is also intriguing, in that Wang’s advice that CCP officials should read Tocqueville’s text occurred right as the anticorruption drive was getting underway.

It’s tempting to dismiss Wang’s apparent interest in and enthusiasm for Tocqueville is a minor idiosyncratic biographical detail, with little connection to his or the CCP’s approach to governance generally, or anticorruption more specifically. But I think his interest in Tocqueville’s work is more significant, and more revealing, about the thinking shaping China’s anticorruption strategy today.

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Will Mongolia’s Presidential Election Put Batbold and Foreign Ownership of the Oyu Tolgoi Mine at Risk?

U. Khürelsükh is the odds-on favorite to win Mongolia’s June 9 Presidential election after an irregular ruling by the Supreme Court denied incumbent President K. Battulga his constitutional right to run for re-election.  Initial predictions were that the election of Khürelsükh, the former Prime Minister and current chair of the Mongolian People’s Party (MPP), would end the investigation into whether corruption infected the deal Mongolia struck with foreign investors on the Oyu Tolgoi mining project, Mongolia’s ticket to economic prosperity. 

The reasoning was that any investigation would implicate former MPP Prime Minister S. Batbold and other senior MPP members.  As this blog has reported (here, here, and here), the evidence of Batbold’s corrupt dealings with the foreign investors in the project, Australian mining giant Rio Tinto and controversial U.S.-Canadian entrepreneur Robert Friedland, seems strong and Batbold’s denials unconvincing.  But the expectation was that the MPP, the lineal descendant of the Marxist-Leninist party that ran the country when it was an appendage of the Soviet Union, still observed the principle of “democratic centralism.” Or as Benjamin Franklin put the principle more colorfully when signing one the foundational documents of true democracy, “We must all hang together, or, most assuredly, we shall all hang separately.”  

The assumption that MPP members would hang together is now at risk thanks to what Khürelsükh said last week on Mongolian TV9’s interview program.

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