7 citations · 12 across the 7 of their papers we have counts for
9 papers
Reinforcement Learning for Micro-Level Claims Reserving
Benjamin Avanzi, Ronald Richman, Bernard Wong +2
Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled cl…
Conditional expectation network for SHAP
Ronald Richman, Mario V. Wüthrich
A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expecta…
Smoothness and monotonicity constraints for neural networks using ICEnet
Ronald Richman, Mario Wüthrich
Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods…
The Probability Conflation: A Reply
Nassim Nicholas Taleb, Ron Richman, Marcos Carreira +1
We respond to Tetlock et al. (2022) showing 1) how expert judgment fails to reflect tail risk, 2) the lack of compatibility between forecasting tournaments and tail risk assessment…
A Discussion of Discrimination and Fairness in Insurance Pricing
Mathias Lindholm, Ronald Richman, Andreas Tsanakas +1
Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not…
A multi-task network approach for calculating discrimination-free insurance prices
Mathias Lindholm, Ronald Richman, Andreas Tsanakas +1
In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protecte…
LocalGLMnet: interpretable deep learning for tabular data
Ronald Richman, Mario V. Wüthrich
Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models…
Embeddings and Attention in Predictive Modeling
Kevin Kuo, Ronald Richman
We explore in depth how categorical data can be processed with embeddings in the context of claim severity modeling. We develop several models that range in complexity from simple…
Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles
M. Merz, R. Richman, T. Tsanakas +1
A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method,…