Data-Driven, Information-Enabled Regulatory Delivery

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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.


Lessons learnt

This chapter lists out the lessons learnt and the key challenges to an increasingly “first choice” approach to regulatory delivery. Problems related to data integration into a single register, reliance on third parties for automatizing solutions and updating of risk classes in a timely interval would have to be resolved. Legal and administrative hurdles also place hindrance on the development of IT tools. In addition to this, IT systems need to fillip the real goals of regulatory delivery – risk management and reduction, to ensure a meaningful protection of key elements of the public welfare – than just weeding out non-compliant actors. The report ends with reference to the guidance existing OECD toolkits and frameworks that can be used in resolving these issues.


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