The global banking industry is grappling with an ever-growing threat of financial crimes, particularly money laundering and sanctions violations. The costs and risks associated with these criminal activities are staggering, with an estimated loss of $1.6 trillion, equivalent to 2.7% of the global GDP, due to money laundering alone. Additionally, the rise in sanctioned entities and the need for comprehensive customer due diligence (CDD) and enhanced due diligence (EDD) have further intensified the challenges for financial institutions. However, there is a glimmer of hope in the form of machine learning (ML). In this article, we will explore how ML can bridge the gap in financial crime compliance and revolutionise the fight against illicit activities.
The Current Landscape
The inadequacy of their existing anti-money laundering (AML) monitoring systems has led to a high number of false positives, resulting in redundant manual investigations and soaring costs for banks. In fact, up to 90% of the alerts generated by these systems are false positives. Furthermore, the constant increase in sanctions worldwide and the need for accurate watchlist screening and KYC/EDD processes pose significant risks for financial institutions. The colossal financial crime compliance expenditure of approximately $214 billion in 2021 highlights the urgency for more effective solutions.
The Power of Machine Learning
Machine learning offers promising advancements over traditional rule-based transaction monitoring systems. ML algorithms have the ability to detect intricate patterns in real-time, making them highly effective in identifying suspicious financial transactions. There are two primary approaches in ML for AML: supervised learning and unsupervised learning.
Supervised learning utilises labelled datasets to train ML algorithms in searching and detecting suspicious transactions. By feeding the algorithm with historical data, the model can learn from past examples and improve its accuracy in classifying new transactions. Approximately 80% of the labelled data is used for training the AML model, while the remaining 20% is used to test its accuracy. This approach enhances the accuracy of rule-based AML systems and enables more precise monitoring.
Unsupervised learning is a powerful approach that explores unstructured transaction records to discover hidden patterns and insights. Through techniques such as clustering, association, and dimensionality reduction, unsupervised learning can identify latent patterns in raw and unlabelled transaction data. This capability enables AML systems to detect and raise alerts based on learned patterns, unravelling even the most sophisticated money laundering schemes.
Extending Surveillance with Machine Learning
Machine learning also extends its impact to other critical areas of financial crime compliance, such as watchlist screening, politically exposed person (PEP) matching, and KYC. ML techniques, including data extraction, pattern matching, and natural language processing, enable banks to automatically retrieve, analyse, and validate customer data from various sources. By establishing relationships between transactions, behavioural patterns, and historical records, ML assists in determining customers’ risk profiles for KYC purposes. Furthermore, ML can analyse unstructured information, contextualise it, and unveil hidden relationships to expose threats posed by politically exposed persons and high-risk entities.
Embracing Machine Learning in Financial Crime Compliance
While machine learning is gaining traction in the financial crime compliance landscape, its widespread adoption in the banking industry faces challenges. Banks must overcome monolithic systems, entrenched frameworks, and governance issues to embrace this transformative technology. Taking a pragmatic and domain-centric approach is crucial in making optimal choices and driving meaningful change in financial crime prevention.
In conclusion, machine learning offers a powerful tool in the ongoing battle against financial crime. By harnessing the capabilities of ML algorithms, banks can enhance their anti-money laundering efforts, improve watchlist screening, and strengthen customer due diligence processes. Although the adoption of machine learning may present implementation challenges, the potential benefits in terms of accuracy, efficiency, and cost reduction outweigh the obstacles. The industry needs to embrace this transformative technology and work towards building robust, adaptive, and transparent financial crime compliance systems to safeguard the integrity of the global banking system.