Risks Special Issue on “Granular Models and Machine Learning Models”
- Modelling of individual claims. This is possible with GM and ML. However, it is a statistical truism that enlargement of the volume of data used does not necessarily increase predictive power. Indeed, in Section 8.2 of my own contribution to this volume, I give an example where it will not. So, can we identify the circumstances in which the use of individual claims is likely to bring predictive benefit?
- Complexity. One might reasonably guess that the answer to the previous question will be somehow related to the complexity of the dataset under analysis. In short, datasets with simple algebraic structures have simple methods of analysis, and complex datasets have more complex methods, and possibly individual claims. So, can we design a metric of data complexity (perhaps based on relative entropy or similar) that could be used to triage datasets?
- Predictive gain. In cases where some predictive gain is found, say reduced prediction error or more granular reserving or some other form of GM/ML supremacy, what exactly is the gain in quantitative terms, and are there any general indications of the circumstances in which it might occur?
- Interpretability. Explainable neural nets (NNs) have entered the literature. These structured NN outputs so as to increase their interpretability. Even so, the results are not always quite transparent. Can we define alternative constraints in the form of output so as to enhance interpretability further?
- Interpretability (continued). In any case, to what extent is interpretability paramount? Can we define circumstances in which it is essential, and others where it does not matter?
Funding
Conflicts of Interest
References
- De Felice, Massimo, and Franco Moriconi. 2019. Claim Watching and Individual Claims Reserving Using Classification and Regression Trees. Risks 7: 102. [Google Scholar] [CrossRef] [Green Version]
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Taylor, G. Risks Special Issue on “Granular Models and Machine Learning Models”. Risks 2020, 8, 1. https://doi.org/10.3390/risks8010001
Taylor G. Risks Special Issue on “Granular Models and Machine Learning Models”. Risks. 2020; 8(1):1. https://doi.org/10.3390/risks8010001
Chicago/Turabian StyleTaylor, Greg. 2020. "Risks Special Issue on “Granular Models and Machine Learning Models”" Risks 8, no. 1: 1. https://doi.org/10.3390/risks8010001