Risk-based regulation for all: The need and a method for a wide adoption solution for data-driven inspection targeting

TL;DR

This paper emphasizes the importance of accessible, transparent, and low-cost data-driven methods for regulators to implement risk-based regulation globally, addressing current challenges and proposing a practical solution.

Contribution

It introduces a novel, effective method for regulation planning that is objective, impartial, transparent, and easy to implement, promoting wider adoption of data-driven inspection targeting.

Findings

The proposed method improves regulation planning accuracy.

It demonstrates applicability to consumer protection.

The approach is computationally efficient and easy to adopt.

Abstract

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.