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EURO – The Association of European Operational Research Societies has a new instrument: the EURO Online Seminar Series (EURO OSS), and this is the page of the EURO OSS on Operational Research and Machine Learning, with website https://euroorml.euro-online.org/ and link to register to the mailinglist at https://forms.gle/YWLb6EPKRQnQert68.
The EURO OSS on Operational Research and Machine Learning is an online seminar series with the goal to brand the role of Operational Research in Artificial Intelligence. The format is a weekly session that takes place every Monday, 16.30-17.30 (CET). It is 100% online-access, and it has leading speakers from Operational Research, as well as neighbouring areas, that will cover important topics such as explainability, fairness, fraud, privacy, etc. We also have the YOUNG EURO OSS on Operational Research and Machine Learning. In each YOUNG session, three junior academics will show their latest results in this burgeoning area.
The EURO OSS on Operational Research and Machine Learning is organized by Emilio Carrizosa (IMUS – Instituto de Matemáticas de la Universidad de Sevilla) and Dolores Romero Morales (CBS – Copenhagen Business School) with the collaboration of PhD students Nuria Gómez-Vargas (IMUS) and Thomas Halskov (CBS). The Online Seminar Series is free thanks to the support given by EURO, as well as Universidad de Sevilla (US) and Copenhagen Business School (CBS). This is gratefully acknowledged.
For the academic year 2024/25, we have confirmed the participation of the following speakers (in alphabetical order)
- Bart Baesens, KU Leuven, Belgium
- Marleen Balvert, Tilburg University, The Netherlands
- Immanuel Bomze, University of Vienna, Austria
- Coralia Cartis, University of Oxford, UK
- Bissan Ghaddar, Technical University of Denmark, Denmark
- Manuel Gómez Rodríguez, Max Planck Institute for Software Systems, Germany
- Vanesa Guerrero, Universidad Carlos III de Madrid, Spain
- Tias Guns, KU Leuven, Belgium
- Georgina Hall, INSEAD, France
- Dick Den Hertog, University of Amsterdam, The Netherlands
- Andrea Lodi, Cornell University, USA
- Jean-Michel Loubes, INRIA, France
- Ruth Misener, Imperial College London, UK
- Luis Nunes Vicente, Lehigh University, USA
- Laura Palagi, Sapienza University of Rome, Italy
- Laurent Perron, Google Research, France
- Veronica Piccialli, Sapienza University of Rome, Italy
- David Pisinger, Technical University of Denmark, Denmark
- Rubén Ruiz, Amazon Web Services, Spain
- Wolfram Wiesemann, Imperial College London, UK
- Yingquian Zhang, Eindhoven University of Technology, The Netherlands
YOUNG EURO OSS on Operational Research and Machine Learning speakers
- Lorenzo Bonasera, University of Pavia, Italy
- Antonio Consolo, Politecnico di Milano, Italy
- Anna Deza, University of California Berkeley, USA
- Margot Geerts, KU Leuven, Belgium
- Sofie Goethals, University of Antwerp/ Columbia University, Belgium/USA
- Thomas Halskov, Copenhagen Business School, Denmark
- Esther Julien, TU Delft, The Netherlands
- Rick Willemsen, Erasmus University Rotterdam, The Netherlands
- Kimberly Yu, Université de Montréal, Canada
The EURO OSS on Operational Research and Machine Learning is the sequel of a weekly online event that we have run January 2021-May2024, https://congreso.us.es/mlneedsmo/. We had more than 100 speakers mainly from Europe and North America. There were more than 2000 colleagues registered to receive updates. For some of our speakers we have had more than 200 attendees, but there are also quite a few colleagues that watch the videos instead at our YT channel. This YT channel has more than 1000 subscribers, and some of the talks in our Online Seminar Series have more than 1,000 views so far.
October 14, 2024, 16.30 – 17.30 (CET)
Using AI for Fraud Detection: Recent Research Insights and Emerging Opportunities
To receive the link to attend the seminar, please register to the mailinglist here.
Abstract:
Typically, organizations lose around five percent of their revenue to fraud. In this presentation, we explore advanced AI techniques to address this issue. Drawing on our recent research, we begin by examining cost-sensitive fraud detection methods, such as CS-Logit which integrates the economic imbalances inherent in fraud detection into the optimization of AI models. We then move on to data engineering strategies that enhance the predictive capabilities of both the data and AI models through intelligent instance and feature engineering. We also delve into network data, showcasing our innovative research methods like Gotcha and CATCHM for effective data featurization. A significant focus is placed on Explainable AI (XAI), which demystifies high-performance AI models used in fraud detection, aiding in the development of effective fraud prevention strategies. We provide practical examples from various sectors including credit card fraud, anti-money laundering, insurance fraud, tax evasion, and payment transaction fraud. Furthermore, we discuss the overarching issue of model risk, which encompasses everything from data input to AI model deployment. Throughout the presentation, the speaker will thoroughly discuss his recent research, conducted in partnership with leading global financial institutions such as BNP Paribas Fortis, Allianz, ING, and Ageas.