IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v7y2021i1d10.1186_s40854-021-00242-4.html
   My bibliography  Save this article

To supervise or to self-supervise: a machine learning based comparison on credit supervision

Author

Listed:
  • José Américo Pereira Antunes

    (Central Bank of Brazil)

Abstract

This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank on-site credit examination to compare the performance of a hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external intervention, with the on-site banking supervision approach of the Central Bank of Brazil. The experiment develops two machine learning classification models: the first model is based on good and bad ratings informed by banks, and the second model is based on past on-site credit portfolio examinations conducted by banking supervision. The findings show that the overall performance of the on-site supervision approach is consistently higher than the performance of the self-supervision approach, justifying the need for on-site credit portfolio examination as conducted by the Central Bank.

Suggested Citation

  • José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
  • Handle: RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00242-4
    DOI: 10.1186/s40854-021-00242-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-021-00242-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1186/s40854-021-00242-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Laeven, Luc & Majnoni, Giovanni, 2003. "Loan loss provisioning and economic slowdowns: too much, too late?," Journal of Financial Intermediation, Elsevier, vol. 12(2), pages 178-197, April.
    2. Bhattacharya, Sudipto & Plank, Manfred & Strobl, Gunter & Zechner, Josef, 2002. "Bank capital regulation with random audits," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1301-1321, July.
    3. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    4. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    5. Stefanadis, Christodoulos, 2003. "Self-Regulation, Innovation, and the Financial Industry," Journal of Regulatory Economics, Springer, vol. 23(1), pages 5-25, January.
    6. Peterson K. Ozili & Erick Outa, 2017. "Bank loan loss provisions research: A review," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(3), pages 144-163, September.
    7. Gorton, Gary, 1988. "Banking Panics and Business Cycles," Oxford Economic Papers, Oxford University Press, vol. 40(4), pages 751-781, December.
    8. Donato Masciandaro & Marc Quintyn, 2013. "The Evolution of Financial Supervision: the Continuing Search for the Holy Grail," SUERF 50th Anniversary Volume Chapters, in: Morten Balling & Ernest Gnan (ed.), 50 Years of Money and Finance: Lessons and Challenges, chapter 8, pages 263-318, SUERF - The European Money and Finance Forum.
    9. De Chiara, Alessandro & Livio, Luca & Ponce, Jorge, 2018. "Flexible and mandatory banking supervision," Journal of Financial Stability, Elsevier, vol. 34(C), pages 86-104.
    10. Craig O. Brown & I. Serdar Dinç, 2011. "Too Many to Fail? Evidence of Regulatory Forbearance When the Banking Sector Is Weak," The Review of Financial Studies, Society for Financial Studies, vol. 24(4), pages 1378-1405.
    11. Jennifer A. Elliott & Aditya Narain & Ian Tower & José Vinãls & Pierluigi Bologna & Michael Hsu & Jonathan Fiechter, 2010. "The Making of Good Supervision; Learning to Say "No"," IMF Staff Position Notes 2010/008, International Monetary Fund.
    12. Stephen S Poloz, 2015. "Integrating Financial Stability into Monetary Policy," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 50(4), pages 200-205, October.
    13. Barth, James R. & Caprio, Gerard Jr. & Levine, Ross, 2004. "Bank regulation and supervision: what works best?," Journal of Financial Intermediation, Elsevier, vol. 13(2), pages 205-248, April.
    14. Goodhart, Charles & Schoenmaker, Dirk, 1995. "Should the Functions of Monetary Policy and Banking Supervision Be Separated?," Oxford Economic Papers, Oxford University Press, vol. 47(4), pages 539-560, October.
    15. Olivier Blanchard, 2009. "The State of Macro," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 209-228, May.
    16. de Moraes, Claudio Oliveira & Montes, Gabriel Caldas & Antunes, José Américo Pereira, 2016. "How does capital regulation react to monetary policy? New evidence on the risk-taking channel," Economic Modelling, Elsevier, vol. 56(C), pages 177-186.
    17. Ozili, Peterson K, 2017. "Bank Loan Loss Provisions Research: A Review," MPRA Paper 76495, University Library of Munich, Germany.
    18. Barth, James R. & Caprio, Gerard, Jr. & Levine, Ross, 2008. "Bank regulations are changing : for better or worse ?," Policy Research Working Paper Series 4646, The World Bank.
    19. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    20. Gerard Caprio & James Barth & Ross Levine, 2008. "Bank Regulations Are Changing: But For Better or Worse?," Center for Development Economics 2008-04, Department of Economics, Williams College.
    21. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    22. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    23. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    24. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    25. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    26. Barth, James R. & Lin, Chen & Ma, Yue & Seade, Jesús & Song, Frank M., 2013. "Do bank regulation, supervision and monitoring enhance or impede bank efficiency?," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2879-2892.
    27. Richard E. Randall, 1993. "Lessons from New England bank failures," New England Economic Review, Federal Reserve Bank of Boston, issue May, pages 13-35.
    28. Martin F. Hellwig, 2014. "Financial Stability, Monetary Policy, Banking Supervision, and Central Banking," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2014_09, Max Planck Institute for Research on Collective Goods.
    29. William R. White, 2006. "Is price stability enough?," BIS Working Papers 205, Bank for International Settlements.
    30. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    31. repec:imf:imfsns:2010/008 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anil Kumar & Suneel Sharma & Mehregan Mahdavi, 2021. "Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review," Risks, MDPI, vol. 9(11), pages 1-15, October.
    2. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.

    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. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    2. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    3. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    4. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    5. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    6. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    7. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    8. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50.
    9. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.
    10. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    11. Roman P. Bulyga & Alexey A. Sitnov & Liudmila V. Kashirskaya & Irina V. Safonova, 2020. "Transparency of credit institutions," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 3158-3172, June.
    12. Revathi Bhuvaneswari & Antonio Segalini, 2020. "Determining Secondary Attributes for Credit Evaluation in P2P Lending," Papers 2006.13921, arXiv.org.
    13. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    14. K. S. Naik, 2021. "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers 2110.02206, arXiv.org.
    15. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Xu Chen & Chunhong Liu & Changchun Gao & Yao Jiang, 2021. "Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    17. Ștefan Ionescu & Nora Chiriță & Ionuț Nica & Camelia Delcea, 2023. "An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans," Sustainability, MDPI, vol. 15(15), pages 1-32, August.
    18. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
    19. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    20. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.

    More about this item

    Keywords

    Bank supervision; Machine learning; Loan loss provisions; On-site credit supervision;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

    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:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00242-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.