IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_666_22.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2022-0666/QEF_666_22.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Redazione, 2016. "Segnalazioni," Economia & lavoro, Carocci editore, issue 1, pages 199-201.
    2. 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.
    3. 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.
    4. 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.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    2. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    3. Hartmann, Philipp & Papaioannou, Elias & Lo Duca, Marco & Heider, Florian, 2007. "The role of financial markets and innovation in productivity and growth in Europe," Occasional Paper Series 72, European Central Bank.
    4. Beck, Roland & Kamps, Annette & Mileva, Elitza, 2007. "Long-term growth prospects for the Russian economy," Occasional Paper Series 58, European Central Bank.
    5. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    6. František Dařena & Jan Přichystal, 2018. "Analysis of the Association between Topics in Online Documents and Stock Price Movements," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1431-1439.
    7. Li Shaoyu & Lu Qing & Fu Wenjiang & Romero Roberto & Cui Yuehua, 2009. "A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-30, October.
    8. González, Fernando & Coppens, François & Winkler, Gerhard, 2007. "The performance of credit rating systems in the assessment of collateral used in Eurosystem monetary policy operations," Occasional Paper Series 65, European Central Bank.
    9. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    10. Matthew Herland & Richard A. Bauder & Taghi M. Khoshgoftaar, 2020. "Approaches for identifying U.S. medicare fraud in provider claims data," Health Care Management Science, Springer, vol. 23(1), pages 2-19, March.
    11. Thimann, Christian & Skala, Martin & Wölfinger, Regine, 2007. "The search for Columbus' egg: finding a new formula to determine quotas at the IMF," Occasional Paper Series 70, European Central Bank.
    12. Wenfa Li & Hongzhe Liu & Peng Yang & Wei Xie, 2016. "Supporting Regularized Logistic Regression Privately and Efficiently," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
    13. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
    14. Kadri Ulas Akay, 2014. "A graphical evaluation of logistic ridge estimator in mixture experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1217-1232, June.
    15. Marco-Antonio Moreno-Ibarra & Yenny Villuendas-Rey & Miltiadis D. Lytras & Cornelio Yáñez-Márquez & Julio-César Salgado-Ramírez, 2021. "Classification of Diseases Using Machine Learning Algorithms: A Comparative Study," Mathematics, MDPI, vol. 9(15), pages 1-21, July.
    16. Pecorari,Natalia Gisel & Cuesta Leiva,Jose Antonio, 2023. "Citizen Participation and Political Trust in Latin America and the Caribbean : AMachine Learning Approach," Policy Research Working Paper Series 10335, The World Bank.
    17. Lambert-Lacroix, Sophie & Peyre, Julie, 2006. "Local likelihood regression in generalized linear single-index models with applications to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 2091-2113, December.
    18. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    19. Heungsun Hwang & Hye Suk & Yoshio Takane & Jang-Han Lee & Jooseop Lim, 2015. "Generalized Functional Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 101-125, March.
    20. Van Rixtel, Adrian & Garcí­a, Juan Angel, 2007. "Inflation-linked bonds from a Central Bank perspective," Occasional Paper Series 62, European Central Bank.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_666_22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.