Leveraging Anti-Money Laundering Controls to Detect and Report Corruption


The major global corruption scandals that have made headlines in recent years, from Malaysia’s 1MDB scandal to the Russian and Azerbaijani laundromats, illustrate how the associated crimes are dependent upon the ability to launder illicit proceeds across borders. As financial institutions grapple with detecting and preventing money laundering, their efforts are resulting in a more unified concept of risk management. One which no longer views money laundering, bribery and corruption, sanction-evading and other financial crimes as separate issues to be dealt with individually, but as an interconnected spectrum of fraud.

In his Pre-Conference session, “Leveraging Anti-Money Laundering Controls to Detect and Report Corruption,” Michael Schidlow, CFE, the head of financial crime risk training and emerging risk advisory for HSBC Bank’s global internal audit department, explained how this evolving understanding of fraud prevention requires organizations to leverage data analytics to keep pace with corruption.

“The reality is [anti-money laundering] is going into a data-enabled marketplace, and if you can’t keep up with data, you’re going to have a hard time moving institutions or moving roles,” said Schidlow.

Effectively using data mining techniques to turn raw data into useful information and incorporate that information into analytic models allows institutions to predict, understand and comprehend what is going on with their customers. That knowledge is essential, as it enables financial institutions to report activities that could be related to crimes of corruption, which have a tremendous impact on the world.

Schidlow highlighted the extent of global corruption, citing the International Monetary Fund’s estimate of $2 trillion in bribes paid every year, as well as the nuance required to launder that much money.

“It’s not as if there is $2 trillion being changed hands in suitcases in dark rooms around the world,” said Schidlow.

Instead, as evidenced in the terabytes of information featured in the Panama and Paradise Papers data leaks, corrupt “politically exposed people” (PEPs) use networks of shell companies, shelf companies, friends, family, and nominees across secrecy jurisdictions to hide the sources of funds and frustrate tracing efforts. Many of the individual transactions and practices are not in and of themselves illegal, but using them to hide illicit proceeds or further another crime is, and financial institutions in particular must be equipped to recognize and report such activity.

“The risk for those in the financial services space is that they are facilitating or not recognizing this activity,” said Schidlow. Failure to spot indications of money laundering or facilitating money laundering and other crimes of corruption can result in major financial or legal penalties for financial institutions for violations of regulations such as the U.K.’s Bribery Act or the Foreign Corrupt Practices Act.

The most important ways in which financial institutions can mitigate their liabilities with regards to money laundering and corruption are screening customers to identify PEPs, and monitoring transactions for risk indicators. However, many organizations in the U.S. don’t do as much as they could, or should.

“The legal framework that exists really only talks about foreign risks. We’re not specifically required to look for domestic PEPs but we are supposed to look for corruption,” said Schidlow.

One of the examples Schidlow used to drive the point home involved U.S. judges receiving kickbacks from private prison companies for giving juveniles unusually long sentences. Since the judges weren’t designated as PEPs, with a higher risk rating, and therefore subject to enhanced monitoring, the red flags exhibited through the transactions associated with an account they shared did not end up prompting a suspicious activity report.

In addition to identifying domestic PEPs, financial institutions need to develop core banking systems that integrate advanced analytics into their transaction monitoring, which will allow them to review more than just the transactions or accounts with the highest risks.

“Most auditors will look at a selection and make a risk-based determination, limited by time and resources, of what to look at. But leveraging analytics allows the review of 100 percent of transactions,” said Schidlow.

And each new scandal or investigation can give fraud examiners new models to incorporate into their analytics. Schidlow described a high-risk model based on reports of North Korea evading sanctions by laundering money with cryptocurrencies.

By incorporating more data, identifying more patterns, looking for the latest indicators of risk and using the analytics-enhanced results to the appropriate authorities, financial institutions can not only limit their liabilities, but help tackle the scourge of global corruption and the economic disparity it fuels. The possibilities for fraud examiners armed with more, better data are reasons for optimism, if you ask Schidlow. “It’s only limited by what you can think to do with the data.”