Artificial intelligence, along with visualizations and network analysis, aid finance professionals in finding and putting a stop to money laundering and a number of other financial crimes. Organizations that indulge in money laundering are in the news once again, and all financial institutions must remain vigilant to new threats.
If you want to help your company track, identify, and squash finance-related crimes, you first have to know what to put in your technology toolkit because the old ways of auditing banking operations can't catch today's criminals. However, a new era of tools is available. Other financial firms are finding and stopping complex criminal activity before it devastates their business.
More and more, artificial intelligence and sophisticated networks are the keys to discovering dispersed and complex webs of illegal activity.
Let's take a look at how they're doing it.
AI Helps Banks Apprehend Criminals
Legacy tools that search for suspicious activity typically use if-then scenarios to build the rules to catch irregular dispersals. For instance, if seven-figure sums move between foreign capitals, an alarm would definitely sound, but any gap in logic is an issue for these straight-forward programs.
For dispersed groups like terrorist organizations, this methodology doesn't work at all. The groups send small payments to many locations around the globe and aren't likely to raise concerns in anti-money-laundering (AML) applications.
Finance leaders in banking are increasingly turning to AI and machine learning as a viable alternative and a way to catch wrongdoers with more robust logic. Machine learning is a spinoff of AI that uses algorithms to find patterns and predict whether certain outcomes are likely. They can even self-direct certain tasks. Banking firms engage in behavioral analytics to catch strange activity in the bank's databases and customer profiles.
This is like using a scalpel to find a needle in a very large haystack. Luckily, at the speed of cyberspace, the process goes much faster than a farmer in his field. QuantaVerse is one institution that is using AI technology for some of the world's largest banks. It's used to identify terror funding, money laundering, and other crimes. So far, this particular technology has caught a Panamanian man on the DEA's shortlist of drug kingpins looking for clean cash.
We are still in the very early days of employing AI in AML systems, but hopes are high that this innovative tech will make investigations more efficient. This would help U.S. banking firms reallocate billions of dollars spent on systems that can't compete with today's sophisticated criminals. This is also a requirement of the Bank Secrecy Act of 1970, requiring finance companies to help Uncle Sam find and stop money launderers.
PwC released a report in April 2017 that found 30 percent of the largest banking entities and 46 percent of the world's largest financial technology organizations have already invested in AI. Machines take multiple data points and dissect them in ways people wouldn't even think of today.
Other banking firms employ AI to prevent fraud. PayPal uses a combination of its AI engine and fraud detectives to stop suspicious activity. This required deep learning software has neural networks that imitate the human mind to find suspicious patterns. Thanks to this technology, PayPal claims its fraud prediction has doubled in accuracy (cutting back on those annoying false alarms that give clients night sweats).
HSBC entered into a contract with Silicon Valley's Ayasdi firm to come up with an automated AML that improves upon a system currently tracked by thousands of people. This is another example of the industry's move toward this robust technology.
Banking execs could be concerned about the capital required to break into AI solutions. The potential for savings down the line doesn't change the fact that this is one of the most expensive transformations a banking firm can make. It also represents a huge cultural shift in an industry that is known for resisting change.
Network Analysis Can Help Spot Financial Crimes
AI isn't the only weapon banks have to find money launderers, corruption, embezzlement, and other criminal actions. Graph technologies give overviews of how entities are interconnected and provide the capability to use complicated queries on large data volumes with real-time results.
This is bad news for those relying on tax havens, wide-flung payments, shell companies, and complex schemes to save them from detection. More and more, banking can rely on AI to find the trail with the most microscopic breadcrumbs.
In order to stop criminals with the resources to plot out complicated strategies, it's not enough to track data about just one suspect entity. Banking crime units must make connections between accounts, companies, individuals, and locations in order to put the whole picture together. Without this complete tapestry, it's hard to make an arrest and find responsible parties.
Banks and financial institutions have to track a number of data sources on every customer, including their various financials actions. Network analyst and visualization experts use technology to index connected data that is easily queried to identify patterns. The associated systems used by these new detectives let firms formulate a consolidated data model that makes pinpointing suspicious trends easier.
IBM offers analytics tools that help large firms identify money laundering activity. IBM (and other firms) uses predictive analytics, Big Data, context computing, and transaction-based systems like Watson to paint a picture of wrongdoing or suspicious activity. Tools like IBM's AML add layers to let banking and financial organizations take advantage of AI tools they need to combat threats and spot suspicious transactions.
Fight Crime With the Right People
If you're ready to catch the bad guys, contact ICS. We can find you the talent you need to successfully combat the threats and find the people who would cause your company harm. Don't let them win. Get started to a safer future by clicking below.