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

Author

Listed:
  • Canio Benedetto

    (Bank of Italy)

  • Sara Crestini

    (Bank of Italy)

  • Alessandro de Gregorio

    (Bank of Italy)

  • Marco de Leonardis

    (Bank of Italy)

  • Andrea del Monaco

    (Bank of Italy)

  • Daniele Gulino

    (Bank of Italy)

  • Paolo Massaro

    (Bank of Italy)

  • Francesca Monacelli

    (Bank of Italy)

  • Lorenzo Rubeo

    (Bank of Italy)

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.

Suggested Citation

  • Canio Benedetto & Sara Crestini & Alessandro de Gregorio & Marco de Leonardis & Andrea del Monaco & Daniele Gulino & Paolo Massaro & Francesca Monacelli & Lorenzo Rubeo, 2025. "Applying artificial intelligence to support regulatory reporting management: the experience at Banca d'Italia," Questioni di Economia e Finanza (Occasional Papers) 927, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_927_25
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2025-0927/QEF_927_25.pdf
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    References listed on IDEAS

    as
    1. Vittoria La Serra & Emiliano Svezia, 2024. "A supervised record linkage approach for anomaly detection in insurance assets granular data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4181-4205, October.
    2. Andrea Carboni & Alessandro Moro, 2018. "Imputation techniques for the nationality of foreign shareholders in Italian firms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), External sector statistics: current issues and new challenges, volume 48, Bank for International Settlements.
    3. Massimo Casa & Laura Graziani Palmieri & Laura Mellone & Francesca Monacelli, 2022. "The integrated approach adopted by Bank of Italy in the collection and production of credit and financial data," Questioni di Economia e Finanza (Occasional Papers) 667, Bank of Italy, Economic Research and International Relations Area.
    4. Marta Bernardini & Paolo Massaro & Francesca Pepe & Francesco Tocco, 2021. "The market notices published by the Italian Stock Exchange: a machine learning approach for the selection of the relevant ones," Questioni di Economia e Finanza (Occasional Papers) 632, Bank of Italy, Economic Research and International Relations Area.
    5. Fabio Zambuto, 2021. "Quality checks on granular banking data: an experimental approach based on machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Micro data for the macro world, volume 53, Bank for International Settlements.
    6. Fabio Zambuto & Simona Arcuti & Roberto Sabatini & Daniele Zambuto, 2021. "Application of classification algorithms for the assessment of confirmation to quality remarks," Questioni di Economia e Finanza (Occasional Papers) 631, Bank of Italy, Economic Research and International Relations Area.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    regulatory reporting; banking reporting; data quality; data management efficiency; data enrichment; information management; statistical production; artificial intelligence; machine learning;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M53 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Training
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Y40 - Miscellaneous Categories - - Dissertations - - - Dissertations

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