On one hand, regulatory bodies are embracing the digital revolution, using every bit of technology at their disposal to centralize their data and investigations, and adopting an enterprise-wide approach in preventing illegal activities from taking place in legitimate financial institutions. On the other, financial criminals are also actively improving the methods they use to infiltrate banking, financial services, and insurance (BFSI) companies and remain undetected as they carry out illegal activities like money laundering, fraud, and terrorist financing. Financial institutions are at the receiving end of these efforts, as they shoulder the increasing cost of compliance, which is typically done by improving their anti-money laundering (AML) and financial crime and compliance (FCC) programs.
Neural networks (NN) are just one of the technologies that regulatory bodies, financial institutions, and third-party service providers are exploring in an effort to make headway against criminal activities. In particular, there are solutions providers that are looking at NN components as the future of AML software. This is because NNs are central to deep learning and having a component that’s capable of deep learning will empower AML solutions to completely capture the nature of an event, improve the accuracy of the model of the event, and remain a step ahead of financial criminal activity.
What is the Role of Neural Networks in Deep Learning?
To put it simply, neural networks refer to computer systems that are inspired by and modelled after the biological brain and nervous system, and these artificial systems are made up of a collection of loosely connected nodes called neural nodes. Just like the neurons in living things, neural nodes can transmit signals to each other. These nodes may also make use of a threshold that determines whether or not the aggregated signal can proceed to the next connection. The signal is transformed as it passes through different neural nodes, and it’s possible for the signal to go through the same nodes multiple times.
Neural networks can be trained, and this is done by feeding them examples with a predetermined input and result. During the learning process, the network will form and remember probability-weighted associations between the input and the target result, and it will adjust its calculations or predictions so that its final output will more closely resemble the predetermined result. After enough adjustments, the NN can meet its user’s criteria to complete this supervised learning process.
How Can Neural Networks Contribute to AML Efforts?
Financial criminals are also developing technologies and strategies to bypass the security systems that are specifically designed to prevent criminal activities. Because of this, banks and other financial organizations find the need to spend more on their AML and FCC programs to remain compliant with the rules set by regulatory bodies. Here’s how the use of NN can make this process quicker and more cost-effective.
Deterministic Scenarios Can Be Used as an Example for Deep Learning
NN-equipped AML solutions need to be trained so that they can adequately adjust the associations between the input and the predetermined output. This can seem like a somewhat unproductive process, but it’s possible to make use of the AML solution even while it’s still in its supervised learning phase. Banks can do this by singling out real-life cases that are highly likely to be instances of financial crime. These include cases that conform to the well-known modus operandi of financial criminals. The user can then feed these positive cases to the machine, giving the machine an opportunity to learn while significantly reducing the human input needed to detect and investigate financial criminal activity.
Deep Learning Can Be Used to Detect Cases That Don’t Conform to Typical Criminal Activities
Once the AML solution has identified the markers that are present in illegal financial activities, it can be put to good use by processing the majority of suspicious cases that the bank handles. This, however, is not the full extent of the capability of NN-equipped AML solutions.
Using other statistical techniques, outlier cases can be clustered and introduced to the AML solution. The system can then analyze the similarities between these cases and determine the elements that set them apart from other, more ‘standard’ crimes. Assigning these anomalies to an AML solution can significantly reduce the workload of human investigators. Also, this can speed up the process of determining the new variables and strategies that criminals are using to take advantage of financial institutions.
Financial crimes continue to change with the times, so BFSI companies must take a proactive approach in preventing criminal activities from taking place within their establishments. The inclusion of neural networks in AML and FCC solutions presents a promising development for financial institutions that aim to avoid being associated with a financial crime without increasing the cost of compliance.