Still mostly NLP and image stuff. Most actual data in the wild is tabular - which GBTs are usually some combination of better and easier. In some circumstances, NN can still work well in tabular problems with the right feature engineering or model stacking.

They are also more attractive for streaming data. Tree-based models can't learn incrementally. They have to be retrained from scratch each time.

Real-time parsing of incoming news events and live scanning of internet news sites - coupled with sentiment analysis. Latency is an interesting challenge in that space.

ML is very good at figuring out stuff like every day at 22:00 this asset goes up if this another asset is not at a daily maximum and the volatility of the market is low.

You might call this overfitting/noise/.... but if you do it carefully it's profitable.