Deep Learning: Your Bank’s Next Big Weapon in Fighting Financial Crime


Before the use of artificial intelligence became mainstream, most people thought of learning as something that could only be done by humans. But now, the sophisticated workings of the human mind are somewhat replicable by machines. One example that illustrates this perfectly is “deep learning,” a concept that comes up frequently in tech circles.

However, if you work in the banking sector and this is your first time reading about deep learning, you may be wondering what it has to do with your industry. With that in mind, here’s a briefer on the deep learning technologies that will soon make a widespread impact in the banking industry, particularly for know your customer (KYC), customer due diligence (CDD), transaction monitoring, and anti-money laundering (AML) policy. Take a deep dive into deep learning and see how it can strengthen your bank’s approach to financial crime.

How Does Deep Learning Work?

“Deep learning” is the term for a sub-field of machine learning that models algorithms like the neural networks of the human brain. This model utilizes a graph format where data points are plotted out as nodes. These nodes are interconnected, just like the anchor points in a spider’s web. The edges are just as important as the nodes, as these are what the machine uses to determine noteworthy relationships between data points.

Like other forms of machine learning, deep learning technologies can incorporate both structured and unstructured data in their analytical processes. But deep learning differs significantly from traditional programming technologies, which interpret data in a linear manner. In contrast to these, systems that rely on deep learning can analyze nonlinear, hierarchical correlations between data points. This results in a 360-degree view of relationships between the nodes, as opposed to a view that pieces together information by examining one node after the other.

Another thing to consider about deep learning is that this kind of machine learning technology doesn’t plateau. This is because layers of nodes all build on previous layers, and thus continue to “deepen” the system’s knowledge. The system is sure to increase its proficiency over time, and is not at risk of peaking—and then stagnating—as it grows older.

Because it allows its users to recognize patterns, detect objects, and translate information that may otherwise be unintelligible, deep learning technology has helped a number of industries harness the full potential of machine learning. One of these, of course, is the banking industry. Banks have now caught on to the idea of using deep learning for their anti-financial crime strategies.

How Can Deep Learning Be Applied in the Context of Preventing Financial Crime?

Money launderers and terrorist financiers may be getting smarter about their methods. But the technology to counter their modi operandi has also become more sophisticated. Graphs of data that are processed with deep learning technologies may eventually reveal interconnected webs of suspicious customer behavior. The connections will not make as much sense if they’re analyzed in a linear manner. But when they’re read by a system that utilizes deep learning, they can indicate that something darker is going on underneath the surface.

To illustrate, let’s take the example of customer enrollment for two different bank accounts. A system that deploys deep learning may pick up on the fact that more than one individual has recently registered for a new bank account with the same distinct password, or that their IP address is the same as another cluster of new registrants. The system can then flag such a relationship and alert the bank’s AML team. In cases like these, the registrants may be part of the same crime ring, or may actually be one person registering for a new account under a fake name.

Another example is the ability of a deep learning system to aggregate data from publicly available information, such as news articles, press releases, and blacklists. The system can reconcile data about money laundering networks and terrorist financing rings from previous years, and then link these with forthcoming information.

Apart from flagging risky customers, the same system will be able to reward the good ones. It can read patterns of behavior from legitimate customers, classify their transactions as consistently unremarkable, and then reduce the number of times that they are flagged. This will allow banks to have smoother onboarding experiences with legitimate customers, while making it difficult for malicious agents to pull through.

It won’t be as difficult as you think for your bank to start using deep learning in its anti-financial crime solution. These artificial neural networks are highly scalable, and a trustworthy AML software vendor will be able to configure the solution with your needs in mind. The onus is on you, however, to champion a more proactive strategy for KYC, CDD, and AML among your staff. This is what will allow you to use the full power of deep learning against money launderers and terrorism backers, and to ultimately keep your institution free from the taint of financial crime.



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