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Access to data and data processing, including the use of machine learning techniques, has become significantly easier and cheaper in recent years. Nevertheless, the same criticisms made to the process of regulatory inspection targeting until the early 2010s remain to this day. This article discusses important aspects concerning regulatory inspection targeting. Inspired by the Service Quality Theory and by the Central Flow Theory, this work proposes a novel parameter for ranking the performances of inspectable entities and shows how to calculate it using a graph signal processing framework. The new approach leads to the proposition of novel evaluation metrics and to a risk-based method for planned regulatory inspection targeting. Statistical simulations illustrate the application of the proposed method to the telecommunications domain. The obtained results evidence a superior performance of the proposed method when compared to other methods, and its potential to be widely adopted by regulators across policy domains and national contexts.

Evaluation metrics and method for planned regulatory inspection targeting