Research into leveraging machine learning to speed up models seems to be gaining traction. This can be useful in computationally-expensive problems such as Greeks for products valued through Monte-Carlo, the pricing of valuation adjustments (CVA, FVA, etc.) or optimal collateral posting. See for example Huge & Savine (2020), Itkin (2020), Henry-Labordère (2019) or Horvath, Muguruza & Tomas (2019). I am however not seeing yet these methods being implemented in the field.

Research into leveraging machine learning to speed up models seems to be gaining traction. This can be useful in computationally-expensive problems such as Greeks for products valued through Monte-Carlo, the pricing of valuation adjustments (CVA, FVA, etc.) or optimal collateral posting. See for example Huge & Savine (2020), Itkin (2020), Henry-Labordère (2019) or Horvath, Muguruza & Tomas (2019).

However I am not seeing these methods being implemented in the field yet. In particular, I know that in some places Automatic Adjoint Differentiation (AAD) has been discarded due to the human and temporal resources involved in rewriting pricing libraries to accomodate this technique.