At first glance, deep learning has the hallmark hype of a technology trend that prudent institutions would do well to avoid. But beyond their celebrated successes in image recognition and translation, there are subtle and persuasive reasons for financial services organizations to deploy deep learning models, even if they haven't used simpler machine learning methods before. Deep learning models can be far more powerful than traditional methods, easier to maintain and faster to develop. These advantages outweigh common concerns over talent shortages and model interpretability. Deep learning models provide several capabilities that are invaluable for financial services applications.

Deep Learning is, as with all technology, neither inherently good nor bad. However, it is not just lunatics in foil hats who are worried that self aware computers could spell danger. The CEO and co-founder of DeepMind himself, Demis Hassabis, has acknowledged that the advanced techniques his own group is pioneering may cause AI to spiral out of human control, and could need to be constrained, while his co-founder, Shane Legg, considers a human extinction due to artificial intelligence the top threat in this century. As a result, contingencies have been put in place. DeepMind investor Elon Musk has just spent 10 million on a study of AI dangers, and Hassabis and his co-founders put in the conditions of Google's takeover that there be an outside board of advisors to monitor the progress of the company's AI efforts.