Interpretable models are an exciting new entry to the ML toolkit. While they are likely sufficient for everyday ML tasks, they might not be expressive enough vs deep networks to tackle complex tasks like fraud and anti-money laundering classification. As with any tool, interpretable vs black models have optimal target applications that data scientist can apply them for. As interpretable models get wider tooling for training, expect them to play a larger role in simpler ML tasks. Black box models are however here to stay and will always have their place with their versatility.