Applying artificial intelligence to support regulatory reporting management: the experience at Banca d'Italia

Canio Benedetto (), Sara Crestini, Alessandro de Gregorio (), Marco de Leonardis (), Andrea del Monaco (), Daniele Gulino, Paolo Massaro (), Francesca Monacelli () and Lorenzo Rubeo ()
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Alessandro de Gregorio: Bank of Italy
Marco de Leonardis: Bank of Italy
Andrea del Monaco: Bank of Italy
Paolo Massaro: Bank of Italy
Francesca Monacelli: Bank of Italy
Lorenzo Rubeo: Bank of Italy

No 927, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: This work describes the approach taken by the statistical function within Banca d’Italia, which manages regulatory reporting data, in using artificial intelligence/machine learning (AI/ML) solutions to support the data management process. It reviews the nine studies carried out so far (six of which have already been implemented in our day-to-day operational processes) to improve statistical processes in three areas: data validation, data enrichment, and process efficiency and automation. For each work, we analyse the business case and the goals, illustrate the solutions identified, and discuss the results. On the basis of the experience gained so far, we draw the main lessons learnt with regard to methodological, organizational, reputational, and procedural implications. Finally, we outline the most promising directions for future research and the implementation of new solutions.

Keywords: regulatory reporting; banking reporting; data quality; data management efficiency; data enrichment; information management; statistical production; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: C32 C38 C45 C81 G21 M15 M53 O33 Y40 (search for similar items in EconPapers)
Date: 2025-04
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https://www.bancaditalia.it/pubblicazioni/qef/2025-0927/QEF_927_25.pdf (application/pdf)

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