Banks in small or poor countries are being shut out as larger global banking organisations move to 'de-risk' their operations. Fearing fines for aiding financial crime, firms are withdrawing from regions or sectors “deemed to pose a high risk of money-laundering, sanctions evasion or terrorist financing”, writes The Economist
Recent data from the Financial Stability Board shows that the number of correspondent-banking relationships - where international banks “clear smaller banks' foreign-currency transactions through big financial centres” - decreased from 2011 to 2016, with the largest declines seen in Eastern Europe (20%). The Central African Republic, Nicaragua and Latvia now have just one correspondent, while correspondent-banking services in Belize and Liberia are completely non-existent.
But it's not just banks that have taken the hit - money-transfer firms and charities are also coming under fire. Chairman of the Association of UK Payment Institutions (AUKPI), Dominic Thorncroft, tells The Economist
that banking is a “major challenge” for 65-70% of money-transfer firms. Meanwhile, research from lobby group Charity & Security Network (C&SN) reveals that two-thirds of charities surveyed have experienced “delayed transfers or account closures”. Remittance handling firms are suffering too, with money transfer issues making it “pricier for migrants to send money home”.
However, there are rising concerns that the de-risking process is actually having the opposite effect - boosting financial crime through increased cash transactions and the use of “informal, unregulated financial networks”, says The Economist
. For this reason, international bodies such as the Financial Action Task Force are attempting to improve their guidance for banks, while the International Monetary Fund is working with local regulators in “severely affected” countries to “improve their financial oversight”.
However, regulators are still threatening non-compliance with substantial fines, meaning 're-risking' is out of the question for most firms. “Both [banks and regulators] are acting rationally, but combined they create a problem that looks intractable,” Tom Keatinge of the Royal United Services Institute tells The Economist
The Economist article
Stock market misdeeds
Meanwhile, larger and more developed economies are also being affected by money laundering. Along with fraud and insider trading, money laundering has been named as one of the most common criminal activities on the Australian stock market, writes ABC News
' Stephen Letts. According to a study by the Australian Transaction Reports and Analysis Centre (AUSTRAC), 21% of the 663 “suspicious matters” reported from the securities and derivatives sector from 2014 to 2016 were related to money laundering - with 51% connected to fraud, and 13% to insider trading.
Firms are withdrawing from regions or sectors deemed to pose a high risk of money-laundering, sanctions evasion or terrorist financing
“We have the second most active stock market in the Asia Pacific region, more than 6.7 million Australians own shares, and there were 929 million trades per day on the ASX worth $4.7bn,” says justice minister Michael Keenan, who is quoted by Letts. “But we know criminal gangs will seek to exploit any weaknesses in our financial systems, putting our economy, national security, and international reputation at risk.”
Foreign criminal gangs have been found to be using Australia as part of money laundering operations in the Asia Pacific. The AUSTRAC study also highlighted the issue of offshore insider trading and market manipulation rorts, being organised from areas such as China, Hong Kong, Canada, Europe and Russia.
ABC News article
Catching the criminals
With money laundering proving to be a key concern for financial markets, regulators and firms across the globe, more advanced detection systems will become essential. According to Wired
's Issie Lapowsky, banks are increasingly using machine learning to identify irregularities in account and transaction data.
“Machines are able to take in multiple additional data points and analyse those data points in a way that may not seem obvious to human beings,” says Kevin Petrasic, a partner at law firm White & Case, who is quoted by Lapowsky.
Traditional software systems, although valuable in their ability to automate the money laundering detection process, often raise the alarm for non-criminal behaviour - nearly 50% of anti-money laundering professionals recently surveyed
by Dow Jones said false positive alerts caused them to lose confidence in the screening process.
Banks are pouring huge amounts of investment into these systems to comply with government regulations, but “the real financial crimes are going unnoticed”, David McLaughlin, founder of data technology firm QuantaVerse, tells Wired
. This is particularly relevant in the fight against terrorism. “The pattern of small transactions a terrorist in hiding makes might not raise red flags for the usual anti-money-laundering systems”, says Lapowsky. “Unless those systems use artificial intelligence.”
In order for systems to become more accurate in detecting criminal behaviour, machines must be trained to recognise good and bad behaviour to identify anomalies - without human supervision. The software developed at QuantaVerse does just this, having analysed data gathered over several years from five major global banking firms, looking at everything from where, how much and how quickly money is moving to irregularities in invoicing number sequences and pre-existing account history. This system was recently used to identify a drug trafficking ring, Grupo Wisa, in Panama by the US Drug Enforcement Administration.
As money laundering, terrorism and other illegal activities become more widespread, banks must play a larger role in cracking down on criminal behaviour - and the use of more advanced data gathering and machine learning methods will be vital in this endeavour. “As human beings slowly adapt to the sneakily ubiquitous threat of terror in our own lives, machines will need to adapt even faster to help choke it off”, Lapowsky concludes.
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