arXiv:2605.18147v3 Announce Type: replace
Abstract: Predictive models are central to credit risk management, where the accuracy of default-probability and loss estimates affects the profitability of...
By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke
The paper studies whether scaling laws similar to those in deep learning apply to actuarial ratemaking with tabular data. Using a motor insurance portfolio, it trains various model families on increasing data fractions and evaluates out‑of‑sample Poisson deviance. Results show all models improve with more data, but TabM outperforms Transformers and MLPs in data scaling, while Transformers need inductive biases to scale effectively.
By Ronald Richman
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke
Mitra‑v2 is a tabular foundation model that achieves state‑of‑the‑art performance on a wide range of real‑world classification and regression tasks, including credit‑risk scoring, clinical prediction, equipment‑failure detection, and house‑price estimation. Trained solely on synthetic data with a larger and more diverse pretraining distribution than its predecessor, it uses a compact 2D Transformer backbone and improved optimization to handle longer contexts and larger feature spaces. On the TabArena and TALENT benchmarks, Mitra‑v2 outperforms leading models such as TabPFN‑3 and TabICLv2, matching the performance of a 1.6B‑parameter TabFM with only 77M parameters, and ranks first on multi‑class classification tasks with more than ten classes.
By Yefan Tao (Bernie), Xiyuan Zhang (Bernie), Xinyi Liu (Bernie), Boran Han (Bernie), Danielle Maddix (Bernie), Haoyang Fang (Bernie), Zhen Han (Bernie), Jiading Gai (Bernie), Xuanqing Liu (Bernie), Michael Bohlke-Schneider (Bernie), Yuyang (Bernie), Wang, Gerald Friedland, Kevan Mah, Chris Lee, Chris Kong
The paper proposes a framework for making machine‑learning based insurance premiums explainable in a way that is valid, actionable, and reliable. It introduces axioms for each property and develops a method that uses Conformal Prediction to filter out uncertain recommendations, ensuring that suggested changes to controllable characteristics are likely to lower premiums. Experiments on real Florida flood‑insurance claims and synthetic driver‑telematics data show that constrained recommendations are more reliable and predict larger risk reductions than unconstrained ones.
By Agathe Fernandes Machado, Fei Huang
arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
By Yi He, Zhengkang Guan, Anpeng Wu, Peng Cui, Fei Wu, Kun Kuang