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A decision-making rule to detect insufficient data quality: an application of statistical learning techniques to the non-performing loans banking data?

Author

Listed:
  • Paolo Cimbali

    (Bank of Italy)

  • Marco De Leonardis

    (Bank of Italy)

  • Alessio Fiume

    (Bank of Italy)

  • Barbara La Ganga

    (Bank of Italy)

  • Luciana Meoli

    (Bank of Italy)

  • Marco Orlandi

    (Bank of Italy)

Abstract

The paper presents a decision-making rule, based on statistical learning techniques, to evaluate and monitor the overall quality of the granular dataset referring to the Non-Performing Loans data collection carried out by the Bank of Italy. The datasets submitted by the reporting agents must display a sufficiently high level of quality before their release to users. The study defines a decision-making rule to distinguish the cases where the corrections applied to the original dataset improve its overall quality from those where the revisions (unexpectedly) make it worse. The decision-making rule is based on a new synthetic data quality indicator, based on past evidence accumulated on data quality management activity, which makes possible the assessment and monitoring of the overall quality of the Non-Performing Loans dataset. The proposed indicator takes into account different metrics that influence the overall quality of the dataset, specifically the number of remarks (potential outliers) detected by the Bank of Italy’s internal procedures, their degree of severity and the expected number of confirmations of underlying data, the latter based on the estimation provided by the logistic regression model.

Suggested Citation

  • Paolo Cimbali & Marco De Leonardis & Alessio Fiume & Barbara La Ganga & Luciana Meoli & Marco Orlandi, 2022. "A decision-making rule to detect insufficient data quality: an application of statistical learning techniques to the non-performing loans banking data?," Questioni di Economia e Finanza (Occasional Papers) 666, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_666_22
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    References listed on IDEAS

    as
    1. Violetta Damia & Carmen Picón Aguilar, 2006. "Quantitative quality indicators for statistics – an application to euro area balance of payment statistics," Occasional Paper Series 54, European Central Bank.
    2. Sarah Cornell-Farrow & Robert Garrard, 2018. "Machine Learning Classifiers Do Not Improve the Prediction of Academic Risk: Evidence from Australia," Papers 1807.07215, arXiv.org, revised Jan 2020.
    3. Picón Aguilar, Carmen & Damia, Violetta, 2006. "Quantitative quality indicators for statistics: an application to euro area balance of payment," Occasional Paper Series 54, European Central Bank.
    4. Redazione, 2016. "Segnalazioni," Economia & lavoro, Carocci editore, issue 1, pages 199-201.
    5. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    potential outliers; non-performing loans; data quality; supervised machine learning; logistic regression;
    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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