Part 6/12:
Unlike retail, the financial sector was slower to adopt analytics due to stringent regulatory requirements and the profitability of existing models. Capital One was an early adopter, utilizing data mining for credit card predictions, but overall banking lagged because of regulatory constraints (e.g., explainability of models) and a perceived lack of necessity.
The 2008 mortgage crisis exemplified both the potential and peril of analytics. Overreliance on predictive models without adequate oversight contributed to overleveraging and risky lending practices. This period exposed how pushing models to their extremes, often motivated by profit motives, can lead to systemic failures, emphasizing the need for responsible use of data science.