5 Ways a Neural Network Helps
Streamline AML Compliance

In recent decades, we’ve discovered the astounding ways that technology can mirror the sophisticated workings of the human brain. One important example is the sub-field of machine learning that’s called deep learning. Deep learning utilizes what are called “artificial neural networks” to process nodes of information in a manner very similar to biological neurons—that is, by passing inputs from one interconnected layer to another.

Though neural networks have been the subject of scientific experimentation since the 1950s, it was in 2012 that a so-called “deep learning revolution” occurred. Computer scientists came to the conclusion that additional layers in the neural network actually enriched the system’s machine learning capabilities, instead of slowing it down. Since then, artificial neural networks have been put to good use in increasingly complex and data-heavy applications, like the anti-money laundering (AML) programs of financial institutions.

The presence of this deep learning technology signals a revolution of sorts for the banking industry, and even small and mid-sized banks may find it beneficial to join in. Artificial neural networks can enhance these banks’ existing AML compliance efforts, and more importantly, serve as a potent secret weapon against money laundering networks and terrorist financiers. If you’re a decision-maker in one of these institutions, below is a compelling argument for onboarding new compliance AML software that harnesses the power of deep learning. Find out how this new technology can streamline your AML program and strengthen your bank’s protection against financial crime.

Why Deterministic and Rules-Based Systems Aren’t Enough to Improve AML Compliance

To date, most banks depend on rules-based and deterministic systems for their AML compliance. That means that the system sets parameters that each case has to meet before it’s classified as one that merits concern. Individual transactions can be tagged as suspicious, then further reviewed by AML staff to determine whether they’re false positives. Oftentimes, the compliance team will aggregate AML data manually before it’s time to present the findings to regulators.

Though this is a straightforward approach to dealing with AML, it betrays the fact that there are many subtleties involved in transaction-related data—especially now that criminals have upgraded their methods. In the current landscape of banking, few piecemeal transactions can actually give the full picture of what’s really going on. While banks are focused on tagging individual cases as false positives or actual leads to financial crime, the more dangerous cases—the false negatives—may have already slipped through the cracks. It’s akin to deducing the ending of a novel through the details provided by only one chapter.

That said, a better alternative lies in using an artificial neural network to detect relationships between transactions. Even small and mid-sized banks could benefit from shifting their AML approach to accommodate deep learning. In fact, they may be better disposed to reap the rewards of deep learning technology than their bigger counterparts, who may already have well-developed AML infrastructure.

The Top 5 Benefits of Using a Neural Network for AML Compliance Efforts

Financial institutions that invest in deep learning-driven AML solutions may see a change in their compliance efforts right away. An artificial neural network will be able to accomplish the following things for a bank’s AML programs:

It Can Build on Deterministic Scenarios
Working with a neural network does not necessarily mean scrapping out all previous AML data gleaned from deterministic scenarios. In fact, the new system can subsume all case data from the previous one, then use the manually labeled “true positives” as reference points for further investigation. All transactions that have some relation to the true positives can then be prioritized by the neural network, thus building off of what the AML compliance team has already done and letting no leads go to waste.

It Can Accommodate Unsupervised Machine Learning When Necessary
Though it’s ideal for banks to aspire towards supervised machine learning with labeled data, a neural network can also help in cases that require anomaly detection through unsupervised machine learning. AML staff can get a head start on their new approach by clustering events or identifying outliers in the data set. These, too, can be read as indicators of suspicious customer behavior. The neural network can support the unsupervised route while banks are still transitioning to a more rigorous supervised learning method.

It Can Enhance Future Machine Learning Capabilities
Some AML staff may worry about onboarding a new system because they assume that introducing more data will be stressful. But one thing that everyone should remember about artificial neural networks is that they get smarter, and thus perform better, when they come across more data. Even as the numbers behind money laundering and terrorist financing activities grow more complex, the AML solution’s machine learning capabilities will grow more sophisticated. Ultimately, it will make it easier—not harder—for AML staff to be compliant, accurate, and responsive in their efforts.

It Can Save Banks Money on Compliance Processes
The technology upgrade to a deep learning-driven solution will admittedly demand a considerable upfront investment. But in the long term, this upgrade could win for its client bank some major savings from efficiency. A bolstered AML solution that uses an artificial neural network can decrease how much the bank spends on compliance-related penalties. It can also bring the bank’s AML compliance efforts up to speed, ensuring that less is spent on meeting evolving compliance standards in the future.

It Can Help Banks Stay Several Steps Ahead of Financial Criminals
Lastly, beyond netting good results from regulators, an artificial neural network can help banks stay faithful to the true purpose of their AML program: to fight financial crime. A system that can read into the subtleties behind money laundering and terrorist financing activities will no doubt keep banks ahead of their nemeses. With an artificial neural network identifying suspicious behaviors and drawing important conclusions, it will be much more difficult for criminals to disguise their dark methods and actually get away with their crimes.

Consider Deep Learning for Your Bank’s AML Program Today

Given the arguments illustrated above, investing in deep learning technology may be a timely move for your bank. Explore an artificial neural network for your bank’s AML program today and ensure its readiness for the challenges of tomorrow.