Data Science & Analytics @ LIRIS, KU Leuven
We are a research group located in Leuven, Belgium working on Data Mining, Data Science and Analytics research, co-located with the department of management informatics at KU Leuven and chaired by prof. dr. Bart Baesens and dr. Seppe vanden Broucke.
Current topics of interest include:
Marketing Analytics: All about the customer
Optimizing the interactions between you and your customers.
Our research focuses on:
- Customer churn (retention) modeling
- Customer segmentation and profiling
- Customer value (CLV) modeling
- Recommender systems
- Engagement modeling
- Profit-driven modeling and model evaluation
- Incorporating social network data
- Novel techniques to optimize lift and to deal with class imbalance
Credit Analytics: New approaches for better credit risk modelling
Given the on-going turmoil on credit markets, a critical re-assessment of current capital and
credit risk modelling approaches is more than ever needed.
Our research focuses on:
- Novel credit risk modelling techniques
- Applying survival analysis techniques to credit risk
- Studying the impact of data quality
Fraud Analytics: Gotcha or Gotch’all?
Fraud has become a billion-dollar business that is increasing every year. The emphasis of our research shifts the focus from fraud detection towards fraud prevention.
Our research focuses on:
- Detecting fraud before it is even committed
- Incorporating networked data to uncover fraudulent patterns
- Detect in real-time when certain processes show characteristics of irregular activities
Process Analytics: Including flows, journeys, and logs
Following flows of data requires appropriate techniques, whether it is a process, a customer journey, or operational logs.
Our research focuses on:
- Industry applications of process mining: telecom industry, banking industry, government
- Applying process mining related techniques to marketing analytics and
customer-centric scenarios such as customer journey mapping - New process quality evaluation metrics for robust conformance checking
- Process clustering techniques to untangle and explains complex models
- Developing predictive analytics techniques towards process-oriented contexts,
e.g. lead-time prediction, executor recommendation systems
Human Resource Analytics: Understand employee behavior
If analytics can be used to study customer behavior, it can also be beneficial to better understand your employee behavior.
Our research focuses on:
- Measuring employee performance and engagement
- Analyzing employee churn and turnover
- Studying workforce collaboration patterns
- Predicting employee absenteeism
- Modeling employee lifetime value
