Up until recently, many decision-makers in the financial sector have been reluctant to try out algorithm-driven technologies like artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). Such hesitation is understandable given the newness and unfamiliarity of these technologies, as well as the hefty price tags that are often associated with their acquisition.
But AI, ML, and ANN are all more affordable and user-friendly than they used to be, and some financial industry leaders have begun using anti-money laundering (AML) platforms with these technologies built in. Doing so has helped banks deploy more preventive and more proactive approaches to issues of financial crime, like money laundering and terrorist financing.
Given what it can do for your own bank’s AML program, an AML compliance solution that utilizes machine learning and neural networks-based technologies may be a viable investment indeed. Read on to learn more about the intertwined technologies of ML and ANN, how these are used to fight financial crime, and what your bank can gain from upgrading to an ANN- and ML-driven solution.
How Do Machine Learning and Neural Networks Operate in the Context of a Bank’s AML Program?
First, let’s define the two key terms “machine learning” and “neural network.” Both machine learning and artificial neural networks are subsets of artificial intelligence, or the branch of computer science that deals with building machines that can simulate the human thought process.
ML is preoccupied with extracting relevant knowledge from the data it’s fed without explicitly being programmed to do so. A platform with ML features can derive knowledge from structured quantitative data, which is represented by numbers and values, or semi-structured data that works with different hierarchies of nested information.
Meanwhile, ANNs are computational networks that are patterned after the biological neural networks of human brains. The artificial neurons in such networks connect with each other like synapses, and the more they are utilized, the better they can get at classifying and clustering complex data in huge volumes.
So, what do these have to do with AML? The answer lies in the data-heavy nature of AML compliance, which involves working with large swathes of information on transaction values, customer details, and the like. When deployed in modern AML programs, ML and ANN technologies can simplify the overwhelming process of sifting through customer and transaction data and help a bank’s human staff distinguish suspicious behavior from normal behavior.
The 4 Biggest Benefits of Onboarding an AML Solution with Machine Learning and Neural Network Technologies
When you make the move to adopt a new AML solution with ML and ANN technologies, here’s what you can expect to change about your bank’s AML compliance efforts and your overall readiness to combat financial crime:
Activation of Adaptive AML Strategies Based on Customer Behavior Models
ML and ANN features can help you and your staff implement a transaction monitoring system that observes—and more importantly, responds to—the most noteworthy customer behavior patterns.
On top of enabling holistic, 360-degree views of the bank’s transactions systems, the ML and ANN features on your AML platform will be able to generate fast and actionable insight into emergent webs of customer behavior. It won’t be long before you and your team can identify conspicuous “clusters” of activity, such as several customer accounts being created at the same time from the same IP address, for example.
Heightened Accuracy and Precision During Financial Crime Investigations
Slowness, inaccuracy, and inconsistency plague many banks’ AML programs, especially those that are still anchored on manual methods for sorting cases. But AML software with ML and ANN features can speed up the work of case investigation while coming up with accurate and precise results. This will make it both easier and faster for your bank’s team to rule out the false positives and to prevent false negatives before the latter take root in your system.
Higher Cost-Efficiency for AML-Related Operations
Though an upgrade to ML- and ANN-driven AML software may require a significant upfront investment on the part of your financial institution, this investment will pay itself off in the long run. You won’t waste as much time or money over long and unproductive AML operations like inefficient case management, and you’ll pay less in penalties to your AML regulators. Before long, you will be able to bring money back into your bank’s coffers, and you will become even better at safeguarding the assets you still have from the taint of criminal activity.
Enhanced AML Performance Over Time
As mentioned above, the longer an ANN- and ML-driven solution is put to use, and the more data-rich its owners’ methods become, the better it will be at protecting its home institution from financial crime. When you invest in these technologies for your AML program, you’ll be rewarded with a smarter, more hardworking, and more adaptable system for financial crime and compliance management—and you’ll be able to avoid the stagnancy that criminals prey on.
New technologies are always intimidating to work with at first. But once you become more familiar with their applications within your industry, it won’t be as hard as you initially thought it would be to implement them. This principle definitely applies to ML and ANN in anti-financial crime operations, so consider taking a deeper dive into these and incorporating them into your long-term AML strategies.