Projects
Innovative credit scoring modeling using textual and social network data. University of Antwerp
Sovereign credit risk models KU Leuven
A correct measurement of credit risk, whether it is captured in a credit rating, a credit spread, or the result of an internal credit rating model, is of crucial importance to several economic agents. For the borrower, the credit risk assessment determines the borrowing cost and the ease of access to financing. For the investor, it reflects the riskiness of an investment opportunity and hence is an indicator for the expected return. For ...
Machine Learning Methods with a Reject Option for Spot Factoring and Beyond KU Leuven
While the design and application of credit risk models has extensively been investigated in operations research literature, the use case of spot factoring received far less attention, despite its subtle differences with typical lending solutions and its increasing importance as a funding solution for higher-risk businesses in a post-financial-crisis, post-COVID-crisis world. Starting from this research gap, we investigate machine learning ...
Academic language proficiency as a predictor of academic achievement. A validity argument of a low-stakes post-entry academic reading and vocabulary screening test for first-year university students. KU Leuven
The transition from secondary to tertiary education is often conceived as difficult, into a new culture with specific rules, conventions and language use (van Kalsbeek & Kuiken, 2014; Wingate, 2015). In Flanders, where, except for Medicine and Dentistry, there are no entry requirements or selection mechanisms, the follow-up of incoming students is important. Success rates of first-year students are low, at the KU Leuven in 2019-2020 for ...
Leveraging Mobile Phone Data and Social Network Analytics for Profit Driven Modeling. KU Leuven
In the rapidly growing world of data science and analytics, data has become an asset that gives companies a competitive edge in fierce and saturated markets. To make the most of all the available data and resources, it is necessary to be mindful of each phase in the analytics process and especially by using the appropriate data with the right techniques and an evaluation that is befitting for the problem at hand. This dissertation considers ...
The Development and Use of Datamining Techniques for Better Decision Making. University of Antwerp
The Development and Use of Datamining Techniques for Better Decision Making. University of Antwerp
Designing Anomaly Detection Algorithms that Exploit Flexible Supervision KU Leuven
Anomaly detection is the task of identifying observations in a dataset that do not conform the expected behavior. It is a crucial data mining task as in the real world, anomalous observations often correspond to real costs. For example, a machine that breaks, a fraudulent credit card transaction, or a patient experiencing irregular heart rhythms. With the advent of big data, manually sifting through millions of observations to detect the ...
Interpretable and robust statistical methods for business applications KU Leuven
Interpretable and robust statistical methods for business applications. Our overall research objective is to develop interpretable ensemble statistical methods with very good performance various business applications, such as fraud detection, credit scoring and churn prediction. It is desirable that the novel methods achieve high interpretability so that the predictions can be explained. Moreover, real life datasets typically consist of mixed ...