What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine’s predictions? Why couldn’t prediction prevent the global financial crisis?
This is a love story about a man named Dan and a bank named Chase, and how they learned to persevere against all odds—more precisely, how they deployed machine learning to empower prediction, which in turn mitigates risk. Exploring this story, we’ll uncover how machine learning really works under the hood.1
Once upon a time, a scientist named Dan Steinberg received a phone call because the largest U.S. bank faced new levels of risk. To manage risk, they were prepared to place their bets on this man of machine learning.
’Twas a fortuitous engagement, as Dan had just the right means and method to assist the bank. An entrepreneurial scientist, he had built a commercial predictive analytics system that delivered leading research from the lab into corporate hands. The bank held as dowry electronic plunder: endless rows of 1’s and 0’s that recorded its learning experience.
The bank had the fuel, and Dan had the machine. It was a match made in heaven. Daydreaming, I often doodle in the margins:
A more ...