Not at all. On the contrary, they have offered opportunities for innovation in areas that are not necessarily in the mainstream of current analytics practice.
A few examples:
A common theme in recent regulations is the demand for “understandability”: having an obligation to explain why a decision was taken. This is a weak point of currently fashionable approaches to predictive analytics (e.g. deep learning), and has forced us to innovate in order to catch up to the automation and predictive power expected of modern analytics, while maintaining or even improving on the understandability of “traditional” modelling.
Good regulations tend to focus on outcomes, and keep their process constraints broad and common-sense-based. Innovation is needed in the way process-based and data-driven analytics are combined to ensure that outcomes are optimised, the optimisation leads to a service level above what the regulator prescribes, and the process leading to it stays within the regulator’s process constraints.
by Panayis Fourniotis Pavlatos,
Director of Intelligent Decisions at QUALCO