Have you tested the idea? Or have you made it up?
If a man can’t find his money in a bank, how can he find it if he can’t reach his bank on the other side of a fence? And if there isn’t anyone around, how can he find his food and his toilet paper?
Those were the questions the government and banks needed to answer to avoid a financial crisis, but the banks had other plans.
With a huge surge in credit, the banks and regulators made it a priority to protect the financial system from their customers. For instance, banks created an “autonomous” network of trust called the Bank of the West, created from an existing network that was designed for a much larger financial institution. Each bank created three regional hubs. By placing those hubs under the management of the Bank of the West, bank officials felt they were able to regulate the banks’ operations and ensure the security of their customers.
When the system wasn’t working as efficiently as it should have, the Bank of the West stepped in to regulate it. The system, which was designed as a sort of “black box,” was designed so that any mistakes could be caught and corrected. It worked well enough to be deployed around the world until problems cropped up.
After the financial crisis of 2008, the idea of artificial intelligence — the ability to self-correct when humans make mistakes — entered the picture. Rather than using a system to make decisions and take measures to safeguard customers’ money, banks wanted to use self-learning algorithms.
This gave rise to machine learning, which enables computers to make decisions based on statistical relationships between data. The more data you have, the stronger the result. And when banks learn from human mistakes or failures, they can take steps automatically to avoid future problems. For instance, if a bank learns a customer had $1,000 in savings, they will take steps to ensure there are no more deposits of similar amounts — instead of taking out $10,000 in loans and handing up the change in cash, they’ll buy $1,000 worth of more expensive credit cards.
Yet this “human-machine-system” approach to regulation doesn’t just apply to banking. Since the Federal Reserve was created in 1913, this regulatory approach has been applied to financial services, in particular derivatives trading, which is essentially the trading of options with derivatives.
With the advent of artificial intelligence and other machine learning algorithms, these systems are now being used to take on complex
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