Partnerships and Chairs

In the banking sector (but not only there), the quality of data an metadata is key to regulatory compliance and the agile development and swift launch of new services and product. The purpose of the chair is to investigate the use of analytics, deep learning en reinforcement learning to create, improve and manage meta-data, with the goal of improving the quality of data and meta-data.

The purpose of the Chair is to promote indepth research tracks concerning predictive analytics, forecast modelling and business process operations. The Chair will contribute to research area by developing new techniques and applications that are able to tackle the requirements of a modern airport.

This research chair builds upon the earlier research chair sponsored by VDAB. The purpose is to study the use of deep learning and advanced feature engineering to optimize the match between job seekers and job applicants. The relevance and impact of external data in facilitating this match is also a topic of research.

The purpose of this research chair is to develop new analytical techniques within the field of insurance. Different application contexts will be studied such as fraud detection, insurance pricing, loss reserving and customer churn prediction. Various criteria will be considered when developing the techniques such as statistical performance and validity, profitability, interpretability and compliance.

The purpose of this research chair is to investigate the use of big data and predictive analytics techniques in the context of different business domains such as Supply Chain Analytics and Customer Lifetime Value Modeling. The focus will be on the entire trajectory, from the theoretical foundations until concrete applications, where the gained insights will be translated into actual business value. The outcome entails innovative techniques for hierarchical sales forecasting across product taxonomies and geographical locations. (Research Chair entry at KU Leuven website)

The purpose of the Chair is to advance the knowledge of how data analytics and robust statistical techniques can be effectively applied in payment fraud and preprocessing and analyzing historical data using advanced robust statistical techniques to detect fraud in an early stage. (Research Chair entry at KU Leuven website)

The purpose of this research chair is to develop innovative recommender systems for Analytics Based Selling. (Research Chair entry at KU Leuven website)

The purpose of this research chair is to investigate the use of big data and predictive analytics techniques in the context of telematics and insurance fraud. (Research Chair entry at KU Leuven website)

This research chair aims to study the strategic impact and corporate governance of analytics. (Research Chair entry at KU Leuven website)

The purpose of this research chair is to develop innovative techniques for Customer Lifetime Value (CLV) modeling. (Research Chair entry at KU Leuven website)

This research chair aims to investigate how the communication with job hunters can be improved, as well as how governmental employment-related control processes can be set-up and executed in an efficient manner.  Research efforts focus on developing recommender systems for on-line job matching. (Research Chair entry at KU Leuven website)

This research chair aimed to develop state of the art forecasting techniques for fraud detection. The outcome consisted of techniques such as “GOTCHA”: a new social network based analytical model for fraud detection. (Research Chair entry at KU Leuven website)

The EngageReaders project investigates the extent to which readers show involvement with a digital newspaper read through a mobile news platform. The result will be a brand-independent predictive model for reader engagement. (More info at Twipe Mobile)

This research chair aimed to developed advanced data analytics method, specifically geared towards obtaining insights in cultural behavioral patterns, e.g. customer’s spending behavior regarding buying tickets for concert venues. The outcome consisted of the development of a new recommender system using Self Organising Maps (SOMs) for an online ticketing provider in the cultural sector. (Research Chair entry at KU Leuven website)