The future of anti-money laundering (AML) controls in the payments industry is increasingly being shaped by machine learning technology. Paysafe’s Giacomo Austin recently spoke with Napier AI to offer valuable insights into this transformation.
Austin, an experienced leader in organisational change, has worked across various industries and global contexts. Previously, he led Paysafe Group’s Compliance team, managing major projects such as mergers, acquisitions, and regulatory compliance efforts. Currently, he coordinates the execution of the Growth roadmap within the Strategy & Transformation unit of Paysafe’s Growth department.
Payment providers today face numerous compliance challenges, including the rise of sophisticated fraudsters and the demand for fast, seamless, and secure payment services. The industry is moving towards cheaper and faster payments while striving to offer more transparency to consumers. However, outdated technology and processes hinder modernisation, and providers must balance these with the need for effective cost management, it said.
Machine learning plays a pivotal role in compliance for all types of payment providers, whether acquirers, merchants, or issuers. Understanding how to leverage this technology for automation and decision-making is crucial. Machine learning can analyse rules and data sets to generate insights into customer behaviour and patterns, enhancing Know Your Customer (KYC), Anti-Money Laundering (AML), and Anti-Fraud processes. It also automates various compliance and risk management tasks.
For instance, machine learning can improve risk assessment and prediction by detecting fraud and assigning risk scores based on patterns identified by algorithms. Automated monitoring models can track transactions in real-time and generate suspicious activity reports (SARs) for compliance teams. Additionally, machine learning aids in the automated creation of compliance reports, ensuring timely and accurate submissions. This technology also helps analysts identify complex patterns in data, whether in customer login information or KYC images. By learning from analyst case resolutions, machine learning models can automate decision-making processes. They can also provide personalised training to compliance teams on regulatory changes and assist in identifying root causes of compliance issues through data analysis.
Machine learning accelerates decision-making by utilising insights from existing rule sets and historical analyst decisions. For example, audit trail analysis can detect irregularities or suspicious activities, allowing for timely compliance violation detection and freeing analysts to focus on more complex investigations.
Building trust in machine learning is vital, achieved through transparent communication about how models function and their objectives. Giacomo Austin of Paysafe emphasises, “Explainability is something that we often talk about in our industry, but it really is the foundation on which we build trust not only within our compliance teams but also within other functions and externally with our regulators and other stakeholders.” Regulatory bodies are becoming more receptive to machine learning in anti-financial crime efforts, provided there is full transparency and explainability. Ensuring compliance with GDPR and other privacy laws is also essential when using customer data and deploying new models.
Addressing biases in machine learning is another critical concern. There is apprehension that machine learning and artificial intelligence (AI) may develop biases in decision-making. Ensuring good data hygiene and managing rules effectively can help mitigate these biases. It is also important to incorporate diverse perspectives into models and have expert staff review decisions.
In summary, the potential of machine learning in compliance is vast, but it requires a commitment to data hygiene, community collaboration, and long-term investment in time and resources. Machine learning is designed to support, not replace, industry experts by providing automation and decision-making insights, thereby enhancing the efficiency of the financial crime-fighting community.
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