AI Thrust Seminar | Graph Machine Learning Practices on Financial Fraud Detection in Post-Pandemic Era
In recent years, graph machine learning methods, especially graph neural network, have achieved remarkable successes in fraud detection in digital financial platform due to their powerful feature extraction and information aggregation capabilities. However, special challenges such as class imbalance or adversarial attack from fraudsters has become important challenges for the further improvement of graph machine learning method-based solutions. How to address the class-imbalance issues in GNN field, how to purify the adversarial attacks in the graph structure and how to enhance the robustness of GNN to improve the generalization ability of out-of-distribution samples have become recent research hotspots. In this talk, I will introduce the research progresses of our research group in dealing with these challenges and discuss some technical trends and promising directions in future.
Dr. Xiang Ao is an Associate Professor in the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). Before joining ICT, he received Ph.D. degree in Computer Science from the Institute of Computing Technology, CAS in 2015 and B.S. degree in Computer Science from Zhejiang University in 2010. His research interests include data mining and NLP techniques for financial applications. He has authored more than 60 referred publications at prestigious international conferences and journals like IEEE TKDE, KDD, WWW, ICDE, SIGIR, ACL, AAAI, IJCAI, etc., and has served as SPC or PC members over top tier international conferences such as KDD, WWW, IJCAI, AAAI, ACL, EMNLP, WSDM, SDM, ICDM, ECML-PKDD, etc. He is supported by the National Key Research and Development Program of China, National Natural Science Foundation of China, CCF-Tencent Rhino-Bird Young Faculty Open Research Fund, Tencent Advertising Rhino-Bird Research Fund, Ant Financial Science Funds, Alibaba Innovative Research Project, Youth Innovation Promotion Association CAS and Beijing Nova Program, etc.