Data-Driven, Information-Enabled Regulatory Delivery

image of Data-Driven, Information-Enabled Regulatory Delivery

Industries and businesses are becoming increasingly digital, and the COVID-19 pandemic has further accelerated this trend. Regulators around the world are also experimenting with data-driven tools to apply and enforce rules in a more agile and targeted way. This report maps out several efforts undertaken jointly by the OECD and Italian regulators to develop and use artificial intelligence and machine learning tools in regulatory inspections and enforcement. It provides unique insights into the background processes and structures required for digital tools to perform predictive modelling, risk analysis and classification. It also highlights the challenges such tools bring, both in specific regulatory areas and to the broader goals of regulatory systems.


The RUCP system and the RAC engine in the Autonomous Province of Trento

This chapter presents the functioning of the RUCP with the RAC engine in Trento Province. Originally conceived as a single register holding data from different inspectorates, the RUCP is now evolving into a system designed to perform risk analysis by segregating businesses as per risk classes and levels. RUCP uses the internationally accepted scorecard tool to estimate risk-based ratings of operators. The chapter also delves into the technical realm and explains the system architecture and the process that goes behind RUCP’s output delivery. The chapter illustrates the system application through the example for an environmental risk assessment for a business holding environmental authorisation. Three regulatory streams i) environment ii) labour iii) agriculture payments, show the areas in which the RAC engine can be developed and also the areas in which data-based analysis can be used. Finally, the chapter notes current limitations in data and recommendations for the future.


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