Crypto is far from the only technological innovation causing problems for financial regulators. Advances in machine learning (ML) are changing how banks allocate credit and manage their own risks internally, how financial advisers construct portfolios, how fraud and suspicious transactions are detected, and how certain back-office operations like customer service are performed. And that is just the beginning. Consumer protection regulators, for example, are concerned that the use of ML algorithms may already be violating fair lending laws.
Although we might hope that automating decisions about who gets credit and on what terms would eliminate discrimination, the reality is that if the data used to train the algorithm reflects human biases, the algorithm will perpetuate those biases.
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