Hey. I've been working on some loan approval and customer retention models at work developed using data from operation data stores (in house databases) within the organization. I've been using tree based models from sklearn which are giving me great results. Since business users in my organization are attracted by buzz words like deep learning and neural networks, we have been having a debate around using deep learning for the aforementioned problems. I personally think deep learning is well suited for problems around NLP and Computer Vision. Moreover, we've also tried deep learning models on the same data set but couldn't get better results than tree based models which further validates my stance. I believe deep learning is very powerful but should be used where it is well suited to the problem you're solving and would outperform algorithms like adaboost, xgboost, random forest, bagging classifier etc. What's your stance on this?