This article, part of a Phd. thesis, uses RF and gradient boosting to predict the risk of adverse selection in health care.
Adverse selection in health care happens when for example when more insurance takers with a prior condition sign up for an insurance. This affects the financial risk of the policy provider.
What strikes me as new is that a lot of economic risk calculation is done using regression modeling. Although the methods researched are not applied in practice, it does show that also for financial risk calculation in health care machine learning is starting to get noted.
What makes the case more reliable is that the dataset is of considerable size, namely 17 million. At this scale a lot of the statistical trade-offs associated with statistical modeling become more or less irrelevant. Or, in any case, that is the discussion.
Remarkably the differences between the conventional approach and random forests and gradient boosting was not that big. But then, with large sums of money, small differences tend to be relevant.
For larger scale decision making with financial decision making I myself have been looking more towards (Bayesian) statistical modeling. The motivation being that the model is simpler to explain, and can incorporate all the uncertainty in the inference. But I guess that if the features make sense a priori, and the sample size is just very big, then explainability and proper uncertainty propagation at some point can loose out to a very good fit.
One of the missed chances in the article is that the differences between the modeling approaches was not tested statistically using several runs of cross validation for example, or another method. This could have added statistical assurance on top of a machine learning method. Just stating a difference without motivating it using the distribution of the difference happens a lot in machine learning; it leaves the whole exercise open ended as far as I am concerned.
From a decision making point of view what I learned from evaluating this article is that comparing models in a statistical valid way, using for example several runs of cross validation followed by a permutation test or Bayesian model, makes the properties of the underlying model less critical.
The article can be found here.