Sure, provided you have enough data to feed a neutral net, and the problem is well suited to it and you don’t mind giving up huge chunks of explainability.

I recently replaced a classifier at work that was using a neural net with a decision tree and some hand chosen features. It performs a bit better, it takes way less time to train and it’s significantly more explainable: my teammates asked why it sometimes miss-classifies a certain edge case, and because the features and model properties were so easy to understand, fixing the issue was a couple of hours work and not a case of “who knows”.