Neural networks rose from the motivation to understand the human brain. By mimicking sinaptic behaviour, they are able to extract meaningful patterns in a layered, sequential way. A good example is how convolutional neural networks recognize object: first these detect edges, then shapes, and eventually understand the object those features form.
By leveraging neural network architectures we are able to find fine details in data which can then be fed to simpler interpretable models.