77 citations · 154 across the 21 of their papers we have counts for
26 papers
Is TabPFN the Silver Bullet for Insurance Pricing?
Bruno Deprez, Wouter Verbeke, Tim Verdonck
Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models (GLMs), with gradient-boosted machines as the leading machin…
Foundation Models for Credit Risk Prediction: A Game Changer?
Bart Baesens, Andreas Goethals, Stefan Lessmann +10
Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has…
Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
Félix Vandervorst, Bruno Deprez, Wouter Verbeke +1
Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud is a prime use case, since fraud…
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Bruno Deprez, Bart Baesens, Tim Verdonck +1
Money laundering, a global problem estimated to account for 2%-5% of the global GDP, required regulators to enforce stringent rules on financial institutions to combat it. One prom…
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Bruno Deprez, Wei Wei, Wouter Verbeke +3
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts o…
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck +1
Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariat…
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
Bruno Deprez, Toon Vanderschueren, Bart Baesens +2
Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to effectively com…
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck +1
In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typi…
Using representation balancing to learn conditional-average dose responses from clustered data
Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets +2
Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to busi…
Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field
Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall litt…
mCube: Multinomial Micro-level reserving Model
Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek +1
This paper presents a multinomial multi-state micro-level reserving model, denoted mCube. We propose a unified framework for modelling the time and the payment process for IBNR and…
Prescriptive maintenance with causal machine learning
Toon Vanderschueren, Robert Boute, Tim Verdonck +2
Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often…
A new perspective on classification: optimally allocating limited resources to uncertain tasks
Toon Vanderschueren, Bart Baesens, Tim Verdonck +1
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain. In credit c…
Noise robustness of persistent homology on greyscale images, across filtrations and signatures
Renata Turkeš, Jannes Nys, Tim Verdonck +1
Topological data analysis is a recent and fast growing field that approaches the analysis of datasets using techniques from (algebraic) topology. Its main tool, persistent homology…
Computational Efficient Approximations of the Concordance Probability in a Big Data Setting
Robin Van Oirbeek, Jolien Ponnet, Tim Verdonck
Performance measurement is an essential task once a statistical model is created. The Area Under the receiving operating characteristics Curve (AUC) is the most popular measure for…
Weight-of-evidence 2.0 with shrinkage and spline-binning
Jakob Raymaekers, Wouter Verbeke, Tim Verdonck
In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of class…
Sparse dimension reduction based on energy and ball statistics
Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck
As its name suggests, sufficient dimension reduction (SDR) targets to estimate a subspace from data that contains all information sufficient to explain a dependent variable. Ample…
direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods
Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck
The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction…
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Sebastiaan Höppner, Bart Baesens, Wouter Verbeke +1
Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection syste…
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Bart Baesens, Sebastiaan Höppner, Irene Ortner +1
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, frau…
Cellwise Robust M Regression
Peter Filzmoser, Sebastiaan Höppner, Irene Ortner +2
The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear mod…
Concordance probability in a big data setting: application in non-life insurance
Robin Van Oirbeek, Christopher Grumiau, Tim Verdonck
The concordance probability or C-index is a popular measure to capture the discriminatory ability of a regression model. In this article, the definition of this measure is adapted…
Multivariate Constrained Robust M-Regression for Shaping Forward Curves in Electricity Markets
Peter Leoni, Pieter Segaert, Sven Serneels +1
In this paper, a multivariate constrained robust M-regression (MCRM) method is developed to estimate shaping coefficients for electricity forward prices. An important benefit of th…
Profit Driven Decision Trees for Churn Prediction
Sebastiaan Höppner, Eugen Stripling, Bart Baesens +2
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of pred…