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Application of classification algorithms for the assessment of confirmation to quality remarks

Author

Listed:
  • Fabio Zambuto

    (Bank of Italy)

  • Simona Arcuti

    (Bank of Italy)

  • Roberto Sabatini

    (Bank of Italy)

  • Daniele Zambuto

Abstract

In the context of the data quality management of supervisory banking data, the Bank of Italy receives a significant number of data reports at various intervals from Italian banks. If any anomalies are found, a quality remark is sent back, questioning the data submitted. This process can lead to the bank in question confirming or revising the data it previously transmitted. We propose an innovative methodology, based on text mining and machine learning techniques, for the automatic processing of the data confirmations received from banks. A classification model is employed to predict whether these confirmations should be accepted or rejected based on the reasons provided by the reporting banks, the characteristics of the validation quality checks, and reporting behaviour across the banking system. The model was trained on past cases already labelled by data managers and its performance was assessed against a set of cross-checked cases that were used as gold standard. The empirical findings show that the methodology predicts the correct decisions on recurrent data confirmations and that the performance of the proposed model is comparable to that of data managers currently engaged in data analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:bdi:opques:qef_631_21
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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2021-0631/QEF_631_21.pdf
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    References listed on IDEAS

    as
    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Tobias Cagala, 2017. "Improving data quality and closing data gaps with machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    3. 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.
    4. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

    1. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.

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    More about this item

    Keywords

    supervisory banking data; data quality management; machine learning; text mining; latent dirichlet allocation; gradient boosting.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • 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

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