I work in the financial world- and to elaborate on your comment:

Speaking strictly for myself, there are a few reasons I rarely use neural networks at my day job:

-Model explainability: Often stakeholders care about being able to explicitly label the "rules" which lead to a specific outcome. Neural Networks can perform well, but it is harder to communicate why the results happened than with simpler models.

-Negligible performance gains: While I am working on multi-million row datasets, the number of features I am working with are often small. The performance improvements I get for running a tensorflow/pytorch model are nearly on par with running an sklearn model. As a result, deep learning is overkill for most of my tasks.

-Developer Time & Speed: It is much quicker and easier to make an effective model in sklearn than it is in tensorflow/pytorch. This is another reason Neural Networks are not my default solution.

There are some out-of-the-box solutions available in tools like Sagemaker or Transformers. But even still, finding and implementing one of these is still just going to take slightly longer than whipping up a random forest.

-Legacy processes: There's a mentality of "if it ain't broke, don't fix it". Even though I am considered a subject matter expert in data science, the finance people don't like me tweaking the way things work without lengthy consultations.

As a result, I am often asked to 'recreate' instead of 'innovate'. That means replacing a linear regression with another linear regression that uses slightly different hyperparameters.

-Maintainability: There are significantly more vanilla software engineers than data scientists/ML Engineers at my company. In the event I bugger off, it's going to be easier for the next person to maintain my code if the models are simple/not Neural Networks.