Deep learning: The next frontier in personalized banking

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In 1997, a computer crossed a new threshold: Deep Blue, which could consider 200 million positions on a chessboard per second, defeated the reigning world chess champion, Gary Kasparov. In 2017, AlphaZero crossed another threshold by crushing Stockfish, the world-champion chess program, scoring 28 wins, 72 draws and zero losses.

AlphaZero is different. Starting only with the rules of chess, it taught itself an unbeatable strategy without the help of programmers—in 24 hours. Instead of considering every possible move, it focuses only on the most promising positions. In other words, it doesn’t simply crunch numbers: it uses “deep learning” to think and learn more like a person, only much, much faster.

Deep learning is a new type of machine learning. It can process a wider range of information—including raw, unstructured data, such as photographs, news stories and handwritten notes. With enough data, deep learning can often produce more accurate results for certain products than traditional machine-learning approaches (for example, the random forecast classifier.)

Interconnected layers of software-based calculators known as “neurons” form a neural network that can ingest and process vast amounts of data through multiple layers that extract increasingly complex features of the data. The network can then use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object from any angle in a new image.

Deep learning is a powerful new tool, but businesses are still learning how to get value from it. We are piloting deep learning techniques in the field of commercial personalization.