Using AI for Fraud Detection: Recent Research Insights and Emerging Opportunities
Distinguished Research Seminar Series
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Date
06 Sep 2024
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Organiser
Department of Industrial and Systems Engineering, PolyU
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Time
16:00 - 17:30
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Venue
Online via ZOOM
Speaker
Prof. Bart Baesens
Remarks
Meeting link will be sent to successful registrants
Summary
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.
Keynote Speaker
Prof. Bart Baesens
Professor
Department of Decision Sciences and Information Management,
KU Leuven
Professor Bart Baesens is a professor of AI for Business at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics. He co-authored more than 250 scientific papers and 10 books some of which have been translated into Chinese, Japanese, Korean, Russian and Kazakh, and sold more than 40,000 copies of these books world-wide. Bart received the OR Society’s Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award for best EJOR paper. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy. Bart is listed in the top 2% of Stanford University's new Database of Top Scientists in the World. He was also named one of the World's top educators in Data Science by CDO magazine in 2021 and has educated tens of thousands of data scientists across the globe in the fields of credit risk, fraud, marketing, ICT, HR and others. Bart also has his own ON-LINE learning BlueCourses platform: www.bluecourses.com which features courses on machine learning, credit risk, fraud, marketing, text analytics, deep learning, web scraping etc.
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