LONDON, UK – Financial institutions and law firms are increasingly turning to machine learning to combat the growing threat of money laundering and other financial crimes. According to industry experts, this advanced technology is a game changer that can help to detect and prevent illegal activities, while also streamlining compliance processes and reducing costs.
The volume of money laundering and other financial crimes is growing worldwide, and the techniques used to evade their detection are becoming ever more sophisticated. To address this challenge, institutions are investing billions each year to improve their defences against financial crime. In 2020, institutions spent an estimated $214 billion on financial-crime compliance, and the resulting regulatory fines related to compliance are surging year over year as regulators impose tougher penalties.
“Traditional rule- and scenario-based approaches to fighting financial crimes have always seemed a step behind the bad guys, making the fight against money laundering an ongoing challenge for law firms,” says Rudi Kesic, CEO at Verify 365. “However, with the emergence of biometric identity verification and machine learning, we now have a powerful tool that can quickly and accurately identify suspicious activity, even in large and complex datasets.”
Machine learning algorithms are designed to learn and adapt from data, which means that they can quickly and accurately identify patterns and anomalies in transaction data. This allows institutions to detect and flag suspicious activity in real-time, reducing the risk of financial crimes going undetected.
According to a report by the United Nations Office on Drugs and Crime, the global market for illicit drugs alone is estimated to be worth around $426 billion. This staggering figure highlights the scale of the challenge faced by law enforcement agencies, regulators, law firms and financial institutions in detecting and preventing financial crimes.
“Technology platforms such as Verify 365 digital client onboarding are a game changer in the fight against money laundering and other financial crimes,” added Rudi. “To realise the full benefits of this technology, institutions need AML technology, strong data science talent, and reliable data sources. With these tools in place, we can create a more effective and efficient AML system that can detect and prevent illegal activity before it becomes a problem.”
Despite the potential benefits of technology and machine learning, there are still challenges to be overcome. For example, institutions must ensure that their data is accurate and up-to-date, and they must also have the necessary talent and expertise to develop and implement this technology effectively.
In addition, regulators and law enforcement agencies must keep pace with the latest advances in machine learning and ensure that they have the necessary tools and resources to detect and prevent financial crimes. Recent fines for non-compliance with anti-money laundering regulations in the UK, Australia, and Canada have highlighted the importance of taking these issues seriously. In 2020, Mishcon de Reya was fined £315,000 by the Solicitors Regulation Authority for failures in its anti-money laundering controls, while in Australia, the Commonwealth Bank was fined A$700 million for serious breaches of AML regulations.
“The fines being handed out by regulators demonstrate that the consequences of non-compliance are serious,” added Rudi. “Law firms must take a proactive approach to AML and ensure that they are using the latest technology and best practices to detect and prevent financial crimes.”
The fight against money laundering and other financial crimes is an ongoing challenge for lawyers around the world. However, with the emergence of technology platforms that use machine learning, there is now a powerful tool that can help to detect and prevent illegal activities, while also streamlining compliance processes and reducing costs. To fully realise the benefits of this technology, law firms must invest in AML technology, strong data science talent, and reliable data sources.
