Johan Bryssinck, Program Lead for AI/ML at Swift, Chalapathy Neti, Head of AI/ML Platform at Swift, & Tobias Hann, CEO Mostly AI – Global Financial Crime Prevention

The discussion focuses on the significant challenges within the financial services industry, particularly concerning financial crime, fraud, and anti-money laundering. These issues are estimated to cost around $480 billion globally. Tackling these problems at scale requires collaborative innovation, primarily because data is dispersed across multiple silos.

Synthetic data emerges as a crucial enabler for this collaborative innovation. Swift, a dominant payment rails company, facilitates a significant volume of global GDP transactions and collaborates with numerous institutions across the globe. To address financial crime effectively, Swift collaborates with partners to utilize synthetic data for innovation responsibly.

Johan elaborates on the persistent issue of fraud, emphasizing the need for collaboration to combat this problem. Despite technological advancements, fraud continues to rise, partly due to the fragmentation of payment methods and the existence of data silos. Swift, in collaboration with the Future of Financial Intelligence Service, has observed significant improvements in fraud detection and prevention through data sharing initiatives.

Swift’s ambition is to build an advanced anomaly detection model for real-time transaction monitoring. Collaboration with banks and leveraging AI in payment control services have already shown promising results, such as a 40% reduction in false positive rates. The next step involves integrating anomaly detection in payment prevalidation services to enhance transaction security before initiation.

Confidential computing is highlighted as a key technology for secure data collaboration, allowing for data protection during all stages of processing. Swift aims to scale this technology globally, working with hyperscalers to reach its extensive customer base.

Toby from Mostly AI discusses the significance of synthetic data in enabling secure and effective data collaboration. Synthetic data, which is fully anonymous, helps in various use cases such as software development, research, and training AI models. It also addresses data privacy concerns and aids in creating more robust and unbiased AI models.

The discussion also touches on the impact of AI on employment and the emergence of new payment companies as potential competitors. Swift continues to innovate by integrating new technologies and expanding its payment rails to adapt to changing financial landscapes.

The conversation underscores the importance of collaborative innovation, AI, and synthetic data in addressing global financial crime. Swift’s ongoing efforts in anomaly detection, secure data collaboration, and synthetic data utilization are pivotal in enhancing the integrity and efficiency of the financial ecosystem.