It is vital for those who have an interest in sustainable energy investment to understand the complexities and economics of energy investment and risk modelling.
This paper provides a novel method, which goes part way to addressing a limitation in apparent standard practice.
We propose a novel method for ensembling GLMs and GBMs and transform the state-of-the-art interpretability technique 鈥� SHAP.
Actuaries are exceptionally good at turning data into decisions. For decades, this has been our competitive advantage.
Balancing homogenous risk groups with credible dataset sizes when selecting a reserving segmentation has long been a challenge for reserving teams.
