Tingting Qiao
Senior data scientist in Adyen, working in the Score team focusing on fraud detection.
Having PhD background in computer vision and natural language processing using deep neural networks. Familiar with prediction models, such as regression, classification models, etc., as well as the latest research techniques, such as adversarial learning, neural networks etc. Several years of experience with popular deep learning frameworks.
Sessions
Fraud is a major problem for financial services companies. As fraudsters change tactics, our detection methods need to get smarter. Graph neural networks (GNNs) are a promising model to improve detection performance. Unlike traditional machine learning models or rule-based engines, GNNs can effectively learn from subtle relationships by aggregating neighborhood information in the financial transaction networks. However, it remains a challenge to adopt this new approach in production.
The goal of this talk is to share best practices for building a production ready GNN solution and hopefully spark your interest to apply GNNs to your own use cases.