I may say this presumptiously but I always laugh when people laud AI and ML in trading. The whole key is picking your features, no matter how fancy your model is you put in garbage, you get out garbage.
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ML seems easier to use in finance for non-trading functions. I've seen some examples of using RNNs for risk calculations
Would be interested to know if they give better risk forecasts than like... a low-dim covariance matrix fitted to historical data with some fatness added to the tails
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I don't think the forecasts are necessarily better but can potentially save on computational cost over monte carlo in some cases
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Also note: probably not useful for equity risk but rather other things in finance
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