IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_611_21.html
   My bibliography  Save this paper

Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting

Author

Listed:
  • Francesco Cusano

    (Bank of Italy)

  • Giuseppe Marinelli

    (Bank of Italy)

  • Stefano Piermattei

    (Bank of Italy)

Abstract

Ensuring and disseminating high-quality data is crucial for central banks to adequately support monetary analysis and the related decision-making process. In this paper we develop a machine learning process for identifying errors in banks’ supervisory reports on loans to the private sector employed in the Bank of Italy’s statistical production of Monetary and Financial Institutions’ (MFI) Balance Sheet Items (BSI). In particular, we model a “Revisions Adjusted – Quantile Regression Random Forest” (RA–QRRF) algorithm in which the predicted acceptance regions of the reported values are calibrated through an individual “imprecision rate” derived from the entire history of each bank’s reporting errors and revisions collected by the Bank of Italy. The analysis shows that our RA-QRRF approach returns very satisfying results in terms of error detection, especially for the loans to the households sector, and outperforms well-established alternative outlier detection procedures based on probit and logit models.

Suggested Citation

  • 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.
  • Handle: RePEc:bdi:opques:qef_611_21
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2021-0611/QEF_611_21.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adrian, Tobias & Shin, Hyun Song, 2010. "Liquidity and leverage," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 418-437, July.
    2. Marcello Bofondi & Luisa Carpinelli & Enrico Sette, 2018. "Credit Supply During a Sovereign Debt Crisis," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 696-729.
    3. Luigi Infante & Stefano Piermattei & Raffaele Santioni & Bianca Sorvillo, 2020. "Diversifying away risks through derivatives: an analysis of the Italian banking system," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 621-657, July.
    4. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    6. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    7. Federico Cingano & Francesco Manaresi & Enrico Sette, 2016. "Does Credit Crunch Investment Down? New Evidence on the Real Effects of the Bank-Lending Channel," Review of Financial Studies, Society for Financial Studies, vol. 29(10), pages 2737-2773.
    8. 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.
    9. Emilia Bonaccorsi di Patti & Enrico Sette, 2012. "Bank balance sheets and the transmission of financial shocks to borrowers: evidence from the 2007-2008 crisis," Temi di discussione (Economic working papers) 848, Bank of Italy, Economic Research and International Relations Area.
    10. 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.
    11. Massimiliano Affinito & Giorgio Albareto & Raffaele Santioni, 2016. "Purchases of sovereign debt securities by Italian banks during the crisis: the role of balance-sheet conditions," Questioni di Economia e Finanza (Occasional Papers) 330, Bank of Italy, Economic Research and International Relations Area.
    12. Jiménez, Gabriel & Ongena, Steven & Peydró, José-Luis & Saurina, Jesús, 2012. "Credit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 2301-2326.
    13. Ricardo Correa & Keshav Garud & Juan M. Londono & Nathan Mislang, 2017. "Sentiment in Central Banks' Financial Stability Reports," International Finance Discussion Papers 1203, Board of Governors of the Federal Reserve System (U.S.).
    14. Fabio Zambuto & Maria Rosaria Buzzi & Giuseppe Costanzo & Marco Di Lucido & Barbara La Ganga & Pasquale Maddaloni & Fabio Papale & Emiliano Svezia, 2020. "Quality checks on granular banking data: an experimental approach based on machine learning?," Questioni di Economia e Finanza (Occasional Papers) 547, Bank of Italy, Economic Research and International Relations Area.
    15. Paolo Massaro & Ilaria Vannini & Oliver Giudice, 2020. "Institutional sector classifier, a machine learning approach," Questioni di Economia e Finanza (Occasional Papers) 548, Bank of Italy, Economic Research and International Relations Area.
    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. Peydró, José-Luis & Polo, Andrea & Sette, Enrico, 2021. "Monetary policy at work: Security and credit application registers evidence," Journal of Financial Economics, Elsevier, vol. 140(3), pages 789-814.
    2. Balduzzi, Pierluigi & Brancati, Emanuele & Schiantarelli, Fabio, 2018. "Financial markets, banks’ cost of funding, and firms’ decisions: Lessons from two crises," Journal of Financial Intermediation, Elsevier, vol. 36(C), pages 1-15.
    3. Lorenzo Burlon & Davide Fantino & Andrea Nobili & Gabriele Sene, 2016. "The quantity of corporate credit rationing with matched bank-firm data," Temi di discussione (Economic working papers) 1058, Bank of Italy, Economic Research and International Relations Area.
    4. Ippolito, Filippo & Peydró, José-Luis & Polo, Andrea & Sette, Enrico, 2016. "Double bank runs and liquidity risk management," Journal of Financial Economics, Elsevier, vol. 122(1), pages 135-154.
    5. Andrea Nobili & Andrea Orame, 2015. "Estimating the effects of a credit supply restriction: is there a bias in the Bank Lending Survey?," Questioni di Economia e Finanza (Occasional Papers) 266, Bank of Italy, Economic Research and International Relations Area.
    6. Fabio Schiantarelli & Massimiliano Stacchini & Philip E. Strahan, 2020. "Bank Quality, Judicial Efficiency, and Loan Repayment Delays in Italy," Journal of Finance, American Finance Association, vol. 75(4), pages 2139-2178, August.
    7. Barone, Guglielmo & de Blasio, Guido & Mocetti, Sauro, 2018. "The real effects of credit crunch in the great recession: Evidence from Italian provinces," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 352-359.
    8. Margherita Bottero & Camelia Minoiu & José-Luis Peydró & Andrea Polo & Andrea F. Presbitero & Enrico Sette, 2019. "Expansionary Yet Different: Credit Supply and Real Effects of Negative Interest Rate Policy," Working Papers 1090, Barcelona Graduate School of Economics.
    9. Labonne, C. & Lamé, G., 2014. "Credit Growth and Bank Capital Requirements: Binding or Not?," Working papers 481, Banque de France.
    10. Viral V Acharya & Tim Eisert & Christian Eufinger & Christian Hirsch, 2018. "Real Effects of the Sovereign Debt Crisis in Europe: Evidence from Syndicated Loans," Review of Financial Studies, Society for Financial Studies, vol. 31(8), pages 2855-2896.
    11. Paolo Del Giovane & Andrea Nobili & Federico M. Signoretti, 2017. "Assessing the Sources of Credit Supply Tightening: Was the Sovereign Debt Crisis Different from Lehman?," International Journal of Central Banking, International Journal of Central Banking, vol. 13(2), pages 197-234, June.
    12. Emanuele Brancati, 2013. "The Real Side of the Financial Crisis: Banks' Exposure, Flight to Quality and Firms' Investment Rate," CEIS Research Paper 302, Tor Vergata University, CEIS, revised 20 Mar 2014.
    13. Ugo Albertazzi & Alessandro Notarpietro & Stefano Siviero, 2016. "An inquiry into the determinants of the profitability of Italian banks," Questioni di Economia e Finanza (Occasional Papers) 364, Bank of Italy, Economic Research and International Relations Area.
    14. Fabio Schiantarelli & Massimiliano Stacchini & Philip E. Strahan, 2017. "Bank quality, judicial efficiency and borrower runs: loan repayment delays in Italy," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Uses of central balance sheet data offices' information, volume 45, Bank for International Settlements.
    15. Bottero, Margherita & Lenzu, Simone & Mezzanotti, Filippo, 2020. "Sovereign debt exposure and the bank lending channel: Impact on credit supply and the real economy," Journal of International Economics, Elsevier, vol. 126(C).
    16. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    17. Kurz, Michael & Kleimeier, Stefanie, 2019. "Credit Supply: Are there negative spillovers from banks’ proprietary trading? (RM/19/005-revised-)," Research Memorandum 026, Maastricht University, Graduate School of Business and Economics (GSBE).
    18. Huremovic, Kenan & Jiménez, Gabriel & Moral-Benito, Enrique & Vega-Redondo, Fernando & Peydró, José-Luis, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," EconStor Preprints 222281, ZBW - Leibniz Information Centre for Economics.
    19. Carlos Cantú & Leonardo Gambacorta, 2019. "How do bank-specific characteristics affect lending? New evidence based on credit registry data from Latin America," BIS Working Papers 798, Bank for International Settlements.
    20. Andrea Orame, 2020. "The role of bank supply in the Italian credit market: evidence from a new regional survey," Temi di discussione (Economic working papers) 1279, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    Keywords

    banks; balance sheet items; outlier detection; machine learning;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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_611_21. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    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: (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.