A Dearth of Data in the De-Risking Debate

As readers of this blog are likely well aware, the fight against grand corruption is closely linked to the fight against money laundering. After all, kleptocrats and others involved in grand corruption need to hide the origins of their ill-gotten wealth. While the criminals who seek to launder their illicit cash are sometimes prosecuted for money laundering, much of the burden of the anti-money laundering (AML) regime falls on banks and other financial institutions. These institutions have obligations to perform due diligence on prospective clients—especially those clients with attributes suggesting high risk—and to report suspicious transaction to the government. Financial institutions can be held liable for failing to fulfill these obligations, and in some cases for their complicity in money laundering schemes. Yet many advocates believe that the current AML framework is not stringent enough, and have called for reforms that would impose additional obligations, and potential liabilities, on the financial institutions that handle clients and transactions that pose a high money laundering risk.

Banks and other skeptics often resist these reforms, arguing not only that the various proposals will do little to reduce money laundering, but also that more stringent AML regulations will lead to a phenomenon known as “de-risking.” This piece of industry jargon refers to the practice of ending or avoiding relationships with individuals or businesses perceived as “high risk” for money laundering. Of course, we want banks to eschew an individual client or transaction with characteristics that suggest a high probability of money laundering. But when banks and others warn about de-risking, they are referring to a phenomenon in which banks refuse to do business with broad categories of clients – for instance, those from particular countries or regions, or in specific lines of business – despite the fact that most of the individuals or firms in that category do not actually present a serious money laundering risk. If the monitoring costs and legal risks associated with certain kinds of accounts are too high relative to the value of those accounts, the argument goes, it’s easier for banks to simply close all of the accounts in the “de-risked” category. But this indiscriminate closure of allegedly risky accounts cuts off many deserving people, firms, and organizations from much-needed financial services.

Is de-risking really a significant problem? Skeptics might observe that the financial industry has incentives to resist more stringent AML regulation, and their warnings of de-risking may be, if not deliberately pretextual, then at least self-serving. That said, other actors, including non-profit groups, have alleged that they have experienced account closures due to de-risking. So the concern is likely a real one. Still, to set rational AML policy, we would want to know not just whether de-risking is a potential problem (it is) or whether it occurs sometimes (it probably does); we would want to know whether it is a systematic and serious problem, one that would likely be exacerbated by a significant enhancement of banks’ AML obligations.

So, what do we know about the extent and magnitude of de-risking in response to AML regulations? The short answer is: not much.

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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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Guest Post: Every Bank Robber Needs A Getaway Car; Banker Held Accountable For Money Laundering

GAB is pleased to publish this analysis by Emile J. M. Van Der Does De Willebois, Coordinator of the World Bank/UNODC Stolen Asset Recovery Initiative, of the significance of a decision of the Gerechtshof Den Haag, the Dutch appeals court in The Hague. As he explains, for too long authorities in the developed world have ignored the role lawyers, bankers, and other “enablers” play in facilitating corruption in the developing world.  Let us hope that the court’s decision marks a turning point in holding them accountable for their role in corruption crimes.  

Last month, a Dutch appeals court ordered the public prosecutor to initiate the criminal prosecution of the former CEO of the nation’s largest bank. The court directed that Ralph Hamers be put on trial for money laundering and other crimes the Amsterdam-based banking giant ING committed during his sevenyear tenure as its chief executive. Financial and legal professionals are rarely prosecuted for crimes they facilitate, and it is even rarer that senior executives, as opposed to the institution they run, are targeted. Until this decision, the indictment of Goldman Sachs bankers for their role in the 1MDB scandal was a notable exception.

The culpability of those who, like the driver in a bank robbery, facilitate a crime is not particularly controversial. We all know that the corruption that happens “over there” needs the services of bankers, lawyers, accountants and other facilitators “over here.” We like to pay lip service to the idea that “it takes two to tango” and acknowledge, at least verbally, that the financial and corporate services in the financial centers of the developed world facilitate the corruption found in large parts of the developing world.

But whether those working on anti-corruption always act upon that notion is another matter. A quick look at the Transparency International corruption perceptions index helps maintain the illusion that the rich developed world is doing well on corruption, and that, looking at the bottom of the table, corruption is really a developing-country problem. We have not really internalized the lessons of the Panama Papers, 1MDB, Danske Bank and, most recently, the FinCEN files, which shone a spotlight on the services provided by banks, lawyers and other professionals in making corruption possible.

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Reforming the US AML System: Some Proposals Inspired by the FinCEN Files

Last week, I did a post with some preliminary (and under-baked) reflections on the so-called “FinCEN Files” reports by BuzzFeed News and the Independent Consortium of Investigative Journalists (ICIJ). These stories relied in substantial part on a couple thousand Suspicious Activity Reports (SARs) that had been filed with the U.S. Treasury Department’s Financial Crimes Enforcement Network (FinCEN), and leaked to a BuzzFeed journalist in 2018. The documents, and the reporting based on them, highlight the extent to which major Western banks assist suspected kleptocrats, terrorists, and other criminal actors move (and launder) staggering amounts of money all over the world, and highlight the deficiencies of the existing anti-money laundering (AML) system.

What can we do to rectify this depressing state of affairs? Much of the commentary I’ve seen so far (both in the FinCEN Files stories themselves, and commentary on the reporting from other sources) emphasizes the need for more individual criminal liability—putting bankers in jail, not just fining banks. Even when banks are threatened or hit with penalties, the argument goes, this doesn’t really have much of a deterrent effect, partly because even what seem like very large monetary sanctions are dwarfed by the profits banks stand to make from assisting shady clients with shady transactions, and partly because the costs of monetary sanctions are mostly passed on to the bank’s shareholders, and don’t really hurt the individuals responsible (or the managers who tolerate, or turn a blind eye to, misconduct).

I’m quite sympathetic to both of these arguments, though with a couple of important caveats. Caveat number one: The absence of individual prosecutions of bankers is sometimes attributed to the fecklessness—or, worse, the “soft” corruption—of federal prosecutors, but as I noted in my last post, I tend to think that the more significant obstacle is the fact that it is very difficult in most cases to prove beyond a reasonable doubt that the that bankers or other intermediaries had the requisite level of knowledge to support a criminal money laundering conviction. Caveat number two: I don’t think we should be too quick to dismiss the idea that levying significant monetary penalties on banks can affect their behavior. After all, these institutions are motivated overwhelmingly by money, so hitting them in the pocketbook is hitting them where it hurts. The problem may be less that monetary sanctions are inherently ineffectual in this context, but rather that they are too low and too uncertain to have a sufficient impact on incentives and behavior.

In that vein, I want to suggest a few legal reforms that might make the U.S. AML system function more effectively. I acknowledge that these are “inside the box” ideas, insofar as they seek to make the existing framework more effective rather than to drastically transform that system. That may make these proposals feel unsatisfying to some, though I suspect the proposals will seem radical, even outlandish, to others. I should also acknowledge that I am not at all an AML expert, so it’s quite possible that the discussion below will contain errors or misunderstandings of the law or the system. But, in the spirit of trying to stimulate further discussion by those who really understand this field, let me throw out a few ideas. Continue reading

Implicit Corruption in the Chinese Consumer Debt Industry? A Close Look at Recent Evidence

While many country’s bribery laws require an express quid pro quo—an agreement to exchange a specific benefit for a specific exercise of government power—in practice many corrupt relationships involve implicit quid pro quos, in which the private party provides something of value to government officials, and the government officials use their power to help their private benefactors, but there is never any express agreement, or even any direct connection between any individual official act and a particular benefit conferred by the private party. The context in which such implicit quid pro quos are most widely suspected and discussed is perhaps campaign finance in democracies, but such implicit quid pro quos can occur in many other contexts as well. It is often very difficult—not only for law enforcement agencies, but also for empirical researchers—to find sufficiently clear evidence of an implicit corrupt deal. Yet quantitative empirical researchers have been making important strides in using available data to detect evidence of hidden or implicit wrongdoing—an approach sometimes dubbed “forensic economics.”

A fascinating recent paper by Sumit Agarwal, Wenlan Qian, Amit Seru, and Jian Zhang (forthcoming in the Journal of Financial Economics) illustrates both the potential and limitations of this approach. The paper, entitled “Disguised Corruption: Evidence from Consumer Credit in China,” presents quantitative evidence of an implicit quid pro quo between a large Chinese bank and government officials who wield regulatory authority over the bank. The paper finds that the bank offers unusually favorable lending terms to government employees (the “quid”) and that in those provinces where this practice is more widespread, the bank receives more favorable treatment from governments (the “quo”). While this evidence alone cannot establish that there was an implicit exchange (the “pro”), the authors suggest that this is the most plausible explanation of the data.

The data is certainly susceptible to that interpretation, but there are other, more benign possibilities. I’ll first say a bit more about the main evidence the paper offers for an implicit quid pro quo, and then suggest (though not necessarily urge) a possible alternative explanation.

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Don’t Believe the Spin on the Mozambican Acquittal

The jury in the federal criminal trial in Brooklyn of  Jean Boustani acquitted him December 2 of charges arising from a scheme to pay Mozambican officials tens of millions of dollars in bribes in return for the government borrowing hundreds of millions of dollars to pay for ships it could not afford. No sooner was the verdict announced than Privinvest — Boustani’s employer, the supplier of the ships, and a major beneficiary of the scheme — crowed it had been completely vindicated.  Despite evidence produced at the trial, charges pending in Mozambique, and allegations in a civil action in the United Kingdom, Privinvest lawyers are telling the press the acquittal proves the company had no part of the scheme.  That it did not pay bribes to win the business.

If it were true the company paid no bribes, three Credit Suisse executives would not have pled guilty to accepting bribes from it in the same court where Boustani was acquitted. Nor would they have named its CEO Iskander Safa, CFO Najib Allam, and Boustani as bribe payers (here). Nor would a trial witness have explained that Government Exhibit 2758, an April 2014 e-mail from Boustani to Allam, is a list of bribes the company paid Mozambican officials.  A list that includes President Filipe Jacinto Nyusi (“Nuy” in the e-mail), former Finance Minister Manuel Chang (“Chopstick”), and former intelligence chief António Carlos do Rosário (“Ros”). (Complete decoded list here.)

No, the verdict of acquittal does not exonerate Privinvest.  Nor anyone else for that matter.  What it shows is two things.

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Lessons from Moldova’s “Theft of the Century”

One year ago today, on April 20th, 2017, a Moldovan businessman named Veaceslav Platon was sentenced to 18 years in prison. His crime? Helping to steal a billion dollars. Between 2012 and 2014, businessmen and politicians siphoned off money from Moldova’s three largest banks in a crime now known as the “Theft of the Century.” While corruption is endemic in many parts of Eastern Europe, the theft in Moldova was spectacular in its size and in the severity of its consequences.

This theft was an economic, social, and political catastrophe for Moldova. The amount of money that disappeared was similar to the amount implicated in the 1MDB scandal in Malaysia–but Malaysia’s GPD is 2.3 times the size of Moldova’s. The Moldovan government’s secret bailout of the banks cost $870 million, one-eighth of Moldova’s GDP. As a result of the theft, three of Moldova’s main banks went bankrupt and were liquidated; more banks are still under the supervision of the National Bank of Moldova, and there is persistent instability in the financial sector. And then there’s the human cost. For example, the misuse of money in the State Health Insurance Company’s accounts led to a medicine shortage in 2014-2015. During street demonstrations that ensued after the theft became public, two dozen people were injured. The political fallout from the theft has also been substantial: Confidence in the government was shattered, as every government branch and every major political party seemed implicated. Furthermore, because the party seen as most heavily involved in the theft was a pro-EU party, Moldovan support for joining the EU plummeted. Pro-Russian sympathizers capitalized on the public reaction, and the pro-Kremlin Igor Dodon was elected president in 2016. Dodon has talked about joining the Russia-controlled Eurasian Economic Union, halted participation in NATO exercises, and opposes the opening of a NATO office in Chisinau, Moldova’s capitol.

The investigation into the theft has dragged. More than 40 people have been implicated, and more prosecutions are supposedly in the pipeline, but only a few people have been convicted so far. With Moldova’s 2018 elections looming, now is a good time to look back at the fallout and lessons from the Theft of the Century.

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The Road Ahead in Anti-Money-Laundering (AML): Can Blockchain Technology Turn the Tide?

One of the most exciting developments in financial and information technology in the past decade is the emergence of so-called blockchain technology. A blockchain is a database of information distributed over a network of computers rather than located on a single or multiple servers. The first and most famous practical application of blockchain technology is the electronic currency Bitcoin. Bitcoin and similar cryptocurrencies using blockchain technologies offer users the equivalent of anonymous cash transactions, and have been linked to illicit transactions in drugs, weapons, and prostitution as they. It is therefore no wonder then that blockchain technology is sometimes viewed as a problem, or at least a challenge, for those interested in fighting financial crime and corruption.

But blockchain technologies have other uses, many of which could in fact aid in the fight against these crimes. In an earlier post on this blog, Jeanne Jeong discussed how blockchain technology could be used managing land records. Another use for blockchain that has occasionally been mentioned (see here and here), but not yet sufficiently pursued, is anti-money-laundering (AML). Currently, banks spend about US$10 billion per year on AML measures, yet money laundering continues to take place on a vast scale. The goal of laundering money is to “wash” illegally obtained money (e.g. through corruption) into “clean” money, making the origins of the money untraceable. Blockchain technologies have five features that could make AML efforts both more effective and less costly:

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Brexit and Anticorruption

So… Brexit. I don’t know nearly enough to weigh in on what this startling development means for European politics, British politics, macroeconomics, Donald Trump’s chances in the U.S. presidential election, or the price of tea in China. But since Brexit is such a major development, I felt like I should say something about the implications for anticorruption, even though that probably wouldn’t be on most people’s top-ten lists of important Brexit implications.

Fortunately, in coming up with something to say about Brexit and anticorruption, I don’t have to work too hard, because two excellent recent posts—one from Robert Barrington at Transparency International UK, another from Corruption Watch—have very nice, clear discussions of the issue. I don’t really have much to add, but let me highlight three of the key worries raised in both posts, and then throw in one more, somewhat more speculative and longer-term question: Continue reading