A comparative analysis of efficiency in the Brazilian banking sector: A data envelopment analysis approach
Vol 6, Issue 2, 2023
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Abstract
This work aims to analyze the efficiency of Brazilian financial institutions until the COVID-19 pandemic period, from production and profitability perspectives. To accomplish this, the data envelopment analysis (DEA) techniques, specifically the CCR and BCC models, are applied to 213 Brazilian financial institutions in four methodological stages. The first step involved conducting a literature review of similar studies. The second step consisted of gathering financial information for each bank through the website of the Central Bank of Brazil. The third step involved selecting the variables to be used in the models. The fourth step was outlier detection using the jackstrap method. Subsequently, the mentioned efficiency models were applied, and the most efficient banks were identified based on each perspective. The results identified heterogeneous groups of efficient banks based on different market segments, with a focus on the efficiency of large banks and public banks when considering the production-oriented perspective. It is also observed that new digital banks are among the banks considered efficient. These findings are valuable for the scientific literature investigating the sustainability of financial institutions, as well as for decision-makers seeking to make more efficient investment allocations and for banking supervisory authorities in formulating risk regulatory policies.
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1. Staub RB, Souza GS, Tabak BM. Evolution of bank efficiency in Brazil: A DEA approach. European Journal of Operational Research. 2010, 202(1): 204-213. doi: 10.1016/j.ejor.2009.04.025
2. Grmanová E, Ivanová E. Efficiency of banks in Slovakia: Measuring by DEA models. Journal of International Studies. 2018; 11(1): 257-272. doi: 10.14254/2071-8330.2018/11-1/20
3. Becker JL, Lunardi GL, Maçada ACG. Efficiency analysis of Brazilian banks: a focus on investments in Information Technology (IT) (Portuguese). Prod. 2003; 13(2): 70-81. doi: 10.1590/s0103-65132003000200007
4. Tabak BM, Krause K, Portella GR. Banking efficiency: the intrinsic value in the production function (Portuguese). Revista de Administração-RAUSP. 2005; 40(4): 361-379.
5. Cava PB, Salgado Junior AP, Branco A. Evaluation of bank efficiency in Brazil: A DEA approach. RAM Mackenzie Management Magazine. 2016; 17(4): 62-84. doi: 10.1590/1678-69712016/administracao.v17n4p61-83
6. Carminati M, Polino M, Continella A, et al. Security evaluation of a banking fraud analysis system. ACM Transactions on Privacy and Security. 2018; 21(3): 1-31. doi: 10.1145/3178370
7. Carminati M, Caron, R, Maggi F, et al. Banksealer: A decision support system for online banking fraud analysis and investigation. Computers & Security. 2015; 53: 175-186. doi: 10.1016/j.cose.2015.04.002
8. Damenu TK, Beaumont C. Analysing information security in a bank using soft systems methodology. ICS. 2017; 25(3): 240-258. doi: 10.1108/ics-07-2016-0053
9. Kumar G, Muckley CB, Pham L, Ryan D. Can alert models for fraud protect the elderly clients of a financial institution?. The European Journal of Finance. 2018; 25(17): 1683-1707. doi: 10.1080/1351847X.2018.1552603
10. Munir U, Manarvi I. Information security risk assessment for banking sector-A case study of Pakistani banks. Global Journal of Computer Science and Technology. 2010; 10(10): 44-55.
11. Saha P, Bose I, Mahanti A. A knowledge based scheme for risk assessment in loan processing by banks. Decision Support Systems. 2016; 84: 78-88. doi: 10.1016/j.dss.2016.02.002
12. Boubaker S, Le TD, Ngo T. Managing bank performance under COVID‐19: A novel inverse DEA efficiency approach. International Transactions in Operational Research. 2022; 30(5): 2436-2452. doi: 10.1111/itor.13132
13. Endri E, Fatmawatie N, Sugianto S, et al. Determinants of efficiency of Indonesian Islamic rural banks. Decision Science Letters. 2022; 11(4): 391-398. doi: 10.5267/j.dsl.2022.8.002
14. Rahman M, Ming TH, Baigh TA, Sarker M. Adoption of artificial intelligence in banking services: An empirical analysis. International Journal of Emerging Markets. 2023; 18(10): 4270-4300. doi: 10.1108/IJOEM-06-2020-0724
15. Ravi H, Vedapradha R. Innovation in banking: Fusion of artificial intelligence and blockchain. Asia Pacific Journal of Innovation and Entrepreneurship. 2021; 15(1): 51-61. doi: 10.1108/APJIE-09-2020-0142
16. Wu H, Yang J, Wu W, Chen Y. Interest rate liberalization and bank efficiency: A DEA analysis of Chinese commercial banks. Central European Journal of Operations Research. 2023; 31(2): 467-498. doi: 10.1007/s10100-022-00817-1
17. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978; 2(6): 429-444. doi: 10.1016/0377-2217(78)90138-8
18. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. 1984; 30(9): 1078-1092. doi: 10.1287/mnsc.30.9.1078
19. Novickytė L, Droždz J. Measuring the efficiency in the Lithuanian banking sector: The DEA application. International Journal of Financial Studies. 2018; 6(2): 37. doi: 10.3390/ijfs6020037
20. Thanassoulis E. Introduction to the Theory and Application of Data Envelopment Analysis. Springer US; 2001. doi: 10.1007/978-1-4615-1407-7
21. Fethi MD, Pasiouras F. Assessing Bank Performance with Operational Research and Artificial Intelligence Techniques: A Survey. SSRN Journal. 2009. doi: 10.2139/ssrn.1350544
22. White PR. Public Transport: Its Planning, Management and Operation, 6th ed. Routledge; 2016.
DOI: https://doi.org/10.24294/fsj.v6i2.2150
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