Prof. dr. Bart Baesens

Professor dr. Bart Baesens holds a master’s degree in Business Engineering (option: Management Informatics) and a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. From a theoretical perspective, he is studying:

  • Neural networks
  • Support Vector Machines
  • Decision trees and decision tables
  • Rule extraction
  • Ant Colony Optimisation
  • Bayesian networks
  • Survival analysis
  • Learning using networked data
  • Model monitoring and backtesting

From an application perspective, he is working on:

  • Customer Relationship Management (churn prediction, response modeling, customer lifetime value (CLV) modeling, cross-selling, …)
  • Credit Risk Management (credit scoring, Basel II, PD/LGD/EAD modeling, model backtesting, benchmarking, stress testing, economic capital calculation, …)
  • Fraud detection (insurance fraud, credit card fraud, anti-money laundering, …)
  • Software engineering (software fault prediction, software effort prediction)
  • Business Process Intelligence and Process mining
  • Web analytics and web mining
  • Social networks

His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is also co-author of the book Credit Risk Management: Basic Concepts, published in 2008. He regularly tutors, advices and provides consulting support to international firms with respect to their data mining, predictive analytics, CRM, and credit risk management policy.

Publications Bart

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+32 16 326884
KU Leuven
Department of Decision Sciences and Information Management
Naamsestraat 69, 3000 Leuven
PhD Thesis
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Developing Intelligent Systems for Credit Scoring Using Machine Learning Techniques