Powering Up With AI And Machine Learning For AML
Anti-money laundering (AML) is a critical part of financial transparency, and a key demand of regulators in markets around the world. As much as USD2tril is laundered globally according to the United Nations Offices on Drugs and Crime—up to 5% of global GDP.
This huge financial black hole requires increasingly sophisticated solutions to address. AML checks in the modern operating environment must incorporate financial events across a wide range of new digital solutions, including the rapidly growing mobile money market, and disruptive financial technologies such as cryptocurrency. That means more data to process in increasingly dynamic operating spheres.
Manual work by AML compliance officers is an important part of tackling the global money laundering landscape, but the reality today is that these human operators and risk teams need the right tools and technology to accurately complete their job.
It’s not just internal resourcing of organizations where AML checks present a challenge in this current landscape. According to Europol, just 10% of suspicious activities filed by financial services institutions are actively investigated by law enforcement agencies, hinting at both the complexity and the resource pressures of this landscape.
An effective AML solution must ensure strict regulatory compliance in this new, high-volume anti-money laundering environment. That’s where machine learning (ML) and artificial intelligence (AI) come into play—offering rapid analysis and automated decisions that strictly adhere to necessary regulatory requirements, providing trusted decision making and clear reporting that helps ensure your enterprise both adheres to the rules, and can readily demonstrate that it does so.
Improved AML checks with AI and ML
More than half (56%) of enterprises have adopted AI in at least one business function according to McKinsey’s 2021 State Of AI Report, up from just 50% the year before. This shows the real direction of travel in use of these technologies to power up business opportunities.
Yet deployment of these technologies is not necessarily uniform across organizations. Despite the encouraging growth in use of AI and ML technologies, risk modeling (16%) and fraud and debt analytics (14%) remain the most underpenetrated spaces according to analysis of the most commonly adopted AI use cases. This represents a substantial gap in delivering value in critical risk areas such as AML compliance.
The cost of compliance for enterprises also continues to soar, with a report by Oxford Economics indicating that AML compliance for financial institutions in the UK alone cost GBP28.7bil in 2021—with an average cost per institution of GBP186.5mil—and is expected to grow to GBP30bil by 2023,
The value of AI adoption is clear, with 78% of AI adopters in the McKinsey report noting a decrease in cost in risk functions as a result of this technology. A remarkable 41% of respondents saw a cost reduction of more than 20%.
AI and ML offer adaptive and self-learning AML solutions that can significantly improve on traditional rules-based systems, reducing false alerts and delivering a more sophisticated risk analysis solution.
The huge growth of mobile money is a prime example of how rules-based systems can quickly lose track of changing market conditions. The number of mobile e-wallets is expected to reach 4.8bil by 2025, representing a huge shift in the financial landscape.
Where rules-based systems will have to be radically rewritten to respond to rapid changes in customer and criminal behavior, an effective AI or ML solution is designed to learn and adapt to these changes, making a truly flexible technology that is ideally suited to this changing environment.
The future of AI and Machine Learning for AML
AI and ML offer wide-ranging benefits to the AML function, including crucial transaction monitoring, customer segmentation and monitoring, automated report generation, automated know-your-customer (KYC), and other vital functionalities.
The benefits of AI and machine learning for AML are increasingly apparent around the world, as operators in a wide range of industries look to embrace these technologies to improve their risk functions.
The findings revealed in McKinsey’s State of AI Report are by no means unique, and pioneering industries such as banking and finance are already demonstrating how these technologies can deliver on their long-held promise.
The COVID-19 pandemic has spurred further adoption of these automated technologies, with more than a third of financial institutions accelerating AI and machine learning adoption for AML checks according to one recent report.
More than a quarter (28%) of financial institutions with assets over USD1bil consider themselves innovators and fast-adopters of these technologies, which they are leveraging due to key drivers of improving the quality of investigations and regulatory filings and reducing false positives and resulting operational costs.
Telecommunications companies are offered a particularly valuable opportunity in this space, with rich data ecosystems that lend themselves to the benefits of AI and ML solutions. Their essential part of the growing mobile money ecosystem will only see this necessity grow in coming years.
Neural Technologies has itself worked with a number of telecommunications firms to implement its advanced Anti-Money Laundering solution across a wide range of markets.
Our solution leverages powerful artificial intelligence and machine learning solutions to help ensure AML compliance, with sophisticated neural network technology to monitor and manage transactions, and undertake complex link analysis to identify high-risk actors or events.
Neural Technologies’ Anti-Money Laundering solution slots seamlessly into existing compliance functions, providing an easily adopted solution that can enhance your AML operations without disrupting vital functions.
The Mobile Money product also operates with advanced KYC functionality that is key to AML checks, meaning enterprises can quickly identify bad actors or high-risk customers, checked against key watch lists produced by regulators around the world.
The money landscape is changing, and with it comes a need to adopt an AML solution that can evolve to those shifts. Machine learning and artificial intelligence technologies can help power efficient AML processes in this changing environment, helping tackle the significant global money laundering challenge, while ensuring your organization remains compliant with tightening regulations.