Money laundering is a big business. The sheer volume of illegal funds being ‘cleaned’ and introduced into the economy is mind-boggling.
Modern laundering is also an unfortunate side-effect of the technological developments which led to online banking, digital currencies, and other related advances.
Financial institutions are now turning to data analytics to improve their anti-money laundering (AML) compliance and fight the launderers. It’s vital for them to do so, as failure to meet AML compliance standards results in heavy penalties.
But how can this process help to tackle the new wave of money laundering?
Doing the laundry
At a basic level, money laundering means to turn illegal funds or assets into ‘clean’ funds, with no apparent link to criminal activity. In the past, criminal enterprises might have used cash-heavy ‘front businesses’ for this purpose. Criminals take in illegal funds and returning ‘clean’ money to unwitting consumers.
Today, things are conversely both more complex and easier. Online fund transfers, digital currencies, and other advances can all be used by launderers. Launderers who now have a portfolio of options for ‘cleaning’ their funds. Many take advantage of different legislation as funds across international borders, for instance.
The numbers soon stack up
Financial institutions deal with huge amounts of data on a daily basis. They must process data sets from both private and public sources. This includes things like financial transaction data and client information. Not to mention also including information from law enforcement bodies concerning suspicious organizations or individuals.
For the purposes of AML, data must be accurately and comprehensively analyzed. Software systems have been used for this purpose for many years, based on rules engines that have been programmed with predetermined rules to determine what’s viewed as a suspicious transaction.
When the system identifies such a transaction within an institution’s data, it’s flagged for AML analysts to investigate whether or not the transaction is a potential case of money laundering.
Data analytics, crime fighter
Data analytics helps to create a more advanced AML system. With a large data set, scientists may harness the power of machine learning to train a neural network to make better sense of huge amounts of raw content.
Machine learning is accomplished via specialized servers, networks, and web hosts (like VPS.NET) to boost processing speeds and performance.
This leads to an Artificial Intelligence-based AML system that can see patterns in financial data. Rather than working to prescribed rules, the system can find clusters of suspicious activity and reliably identify when a potentially fraudulent transaction is actually no such thing.
For instance, a customer might send a high dollar transfer to a high-risk jurisdiction. A rules engine would often flag the transaction, even though the customer might have legitimate business in that jurisdiction. If they regularly transfer funds there, an intelligent AI system using data analytics would recognize that pattern of activity and approve it.
The benefits to businesses
Data analytics and machine learning can be harnessed to produce intelligent anti-money laundering systems.
Such systems have a range of advantages over traditional solutions:
- Intelligent recognition of patterns within financial data analytics reduces the number of ‘false positives’. Traditional systems often flag legitimate transactions as needing investigation.
- Machine learning models are flexible. They can adapt to new data, requiring less human input once they’re trained correctly.
- Analytics platforms can analyze a much larger volume of data. They do so in less time and at a fraction of the cost of traditional systems.
- Diverse data sets can be integrated and combined at pace. This allows for a more rounded picture of transactions and/or individuals to be generated.
- New insights discovered within data can be used to devise additional ways to manage suspicious activity.
Anti-money laundering tactics are just one of the thousands of ways that data processing is making the world a better place. From improved science and medical insight to easier transportation and daily living, high-performance computing and data analytics are changing the way we live.
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