Fraud Networks

Complex systems often make more sense once we map the relationships inside them. We talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal networks. Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025.

Guest

Bavo DC Campo: I am a data scientist and a statistician, who tries to combine the best of two worlds. I have over 10 years of experience and my area of expertise lies in the construction and validation of predictive models. I also have experience in other research areas, such as clustering and assessing the agreement or reliability of repeated measurements. Further, I am proficient in and have a passion for programming (especially in R). I love to immerse myself in new research projects, which require me to solve complex problems and to analyze the data thoroughly. Tackling new, unfamiliar challenges is something I look forward to as it allows me to grow and develop new knowledge and new skills. I did my PhD in Actuarial Science at the KU Leuven, under the supervision of professor dr Katrien Antonio. My research focused on the development and evaluation of predictive modeling techniques within actuarial science. In my research, I assessed the performance of both statistical and machine learning methods. Another part of my research focused on the development of fraud detection models using social network features. Here, I examined both the methodological and practical part. If you want to read my PhD thesis, you can do so by clicking on this link. Before my PhD, I worked as a biostatistian in the International Ovarian Tumor Analysis (IOTA) group and I stayed connected to biomedical research throughout my whole career. In the IOTA group, I was part of an interdisciplinary team of researchers connected to UZ Leuven and KU Leuven. I worked on numerous projects, which provided me with a firm and solid basis in biostatistics. Furthermore, my main research projects focused on the development and validation of clinical prediction models.