14:30
October 24 - 14:30 - "Bruno Deprez & Olivier Caelen" -
14h30:
"Network-based anti-money laundering"
Abstract:
Network analytics has proven to be essential for anti-money laundering (AML), since money laundering schemes span many transactions between multiple parties. One of the main challenges in the field is that fraudsters try to evade detection by constantly adapting their laundering methods. This means that financial institutions need to fine-tune and update their internal AML models on a regular basis. However, computing power is the main bottleneck since banks typically need to monitor millions of transactions. Therefore, continual (graph) learning is becoming an essential tool in having an effective AML system. This talk will discuss some recent work by giving an overview of the current methods applied for network-based AML and it will dive into the key aspect of continual graph learning. The topics and ideas will be illustrated on an AML use case.
15h30: Coffee Break
16h00:
"Machine Learning for Payment Fraud Detection"
Abstract:
Payment fraud remains a major challenge for financial institutions, requiring models that can learn from vast and complex transaction streams. This presentation will explore how machine learning can be applied to detect fraudulent credit and debit card payments. After introducing the context of card fraud and the operational constraints of (near) real-time detection, we will examine the main methodological challenges like concept drift, sampling bias, class imbalance, and others... We will then conclude with a discussion on how techniques such as clustering and graph mining can be use to build more robust features, improve detection accuracy and reduce false positives.