Banks are always free from terrorist attacks due to numerous reasons. This doesn’t mean that banks have had exclusive cases of terrorist attacks but terrorists prefer manipulating banks to fund their activities than burning them down. They understand that a criminal attack on a bank means they are joking with their source of strength. Terrorism all over the world is supported with huge amount of money. So, they will rather use force and threat to get money from banks instead of blowing them off. Terrorists also try to source money by themselves. For instance, the ISIL engaged in oil bunkering and were making around $1.3 million per day. Sometimes, they keep their money on their own without taking them to bank due to the fear of unknown while some other times, they make use of banks, especially if the transaction to be made has to do with a long distance.
One major factor makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network—activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems (Lapowsky, 2017).
Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job. They rely upon hard-coded “if-then” rules about predictably suspicious behavior. If the software spots a seven-figure transfer of funds from Miami to Bogota, for example, it knows to flag it. But as terrorist groups like ISIS recruit people internationally for smaller, targeted attacks, those tools become far less effective.
The pattern of small transactions a terrorist in hiding makes might not raise red flags for the usual anti-money-laundering systems. Unless those systems use artificial intelligence (Lapowsky, 2017).
Banks are increasingly turning to machine learning to mine vast quantities of bank data and find anomalies in accounts and transactions that might otherwise have gone unnoticed. “It’s a surgical approach to finding a needle in a haystack,” says Stitt, who now serves as director of financial crime analysis for the Wayne, Pennsylvania-based firm QuantaVerse, which developed the AI technology some of the world’s biggest banks use to identify money laundering, terrorist funding, and other financial crimes. The technology has already helped identify a Panamanian man the DEA called “one of the world’s most significant drug money launderers” (Lapowsky, 2017).
Even though the machine is still in its early days and not too many confirmations have been made about its effectiveness, it is essential to note that banks are burdened with the responsibility of helping authorities to fish out criminals all over the world. Software has helped automate that process somewhat. Yet, the process is beset by false positives, in which the system flags behavior that is not actually criminal. A recent Dow Jones survey of more than 800 anti-money laundering professionals found that nearly half of them said false positive alerts hurt their confidence in the accuracy of the screening process. Still, to comply with governments, banks invest billions of dollars in these systems every year. “That’s billions invested—a lot of humans investigating the flags a legacy system will generate, and a large majority of those turn out not to be financial crimes,” says David McLaughlin, who founded QuantaVerse in 2014. “Meanwhile, the real financial crimes are going unnoticed” (Lapowsky, 2017). This means that terrorists are still winning in their subtle attack over banks.
The challenge, particularly for banks looking to stop the flow of money to foreign fighters, is that there are infinite possible permutations of transactions to hand code into a rules-based system. A person looking to join ISIS might take $80 out of an ATM in Brussels, receive a wire transfer in Algeria, and use a credit card in Lebanon. He might take out a payday loan or transfer money to family. On their own, these incremental activities might not trigger suspicion, but taken together, they create a pattern that a machine might identify as fishy. Let me emphasize at this point that human involvement in every stage of this demanding assignment cannot be wished off. If not, terrorists will always take time to master the system of the software and in no meantime, they will come up with a way to bypass it. So, the billions invested will turn out a waste as usual. This is a point that authorities have always missed it but have decided not to give a consideration. If this loophole can be blocked, then the banking sector is on the verge of recording a resounding victory over terrorists.
However, QuantaVerse is proving to be different from those traditional software that can be easily maneuvered. This new software learns the ‘unimaginable’ predictors on its own. The company’s team of data scientists trained its algorithms on several years’ worth of data from one of the top five biggest banks in the world, whose name the company is contractually prohibited from sharing publicly. With Stitt’s input, the team trained the system in what good and bad behavior looks like so that the system could begin learning and identifying that behavior without human oversight.
These judgment calls are based on a combination of factors, including how quickly money moves around, where it’s moving, and how much is being transferred. But they also look for clues like anomalies in invoicing number sequences. If a criminal group is looking to launder money, it might falsify invoices to make it appear a legitimate transaction occurred, when, in fact, the money came from a drug deal or the sale of counterfeit goods. Those invoices come with their own identification numbers, and often, Stitt says, “People forget which numbers they used.” QuantaVerse’s technology can spot duplications and mistakes in the system (Lapowsky, 2017). Hence, for now, it is the most reliable means of waging war against terrorists in the banking sector.
References
HNB (2015).Prevention of Money Laundry and Terrorist Financing. Retrieved from https://www.hnb.hr/en/core-functions/supervision/prevention-of-money-laundering-and-terrorist-financing
Lapowsky, I (2017). Banks Deploy AI to Cut Off Terrorists’ Funding. Retrieved from https://www.wired.com/story/quantaverse-ai-terrorist-funding/
Schippa, C (2019). This is How Terrorists Finance Their Attacks. Retrieved from https://www.weforum.org/agenda/2017/11/terror-attacks-are-increasingly-self-funded-how-can-we-stop-them/