  Access to data and data processing, including the use of machine learning
techniques, has become significantly easier and cheaper in recent years.
Nevertheless, solutions that can be widely adopted by regulators for market
monitoring and inspection targeting in a data-driven way have not been
frequently discussed by the scientific community. This article discusses the
need and the difficulties for the development of such solutions, presents an
effective method to address regulation planning, and illustrates its use to
account for the most important and common subject for the majority of
regulators: the consumer. This article hopes to contribute to increase the
awareness of the regulatory community to the need for data processing methods
that are objective, impartial, transparent, explainable, simple to implement
and with low computational cost, aiming to the implementation of risk-based
regulation in the world.
