Machine Learning-driven Group Ranking in Data Envelopment Analysis: Applications in the Banking Sector
الموضوعات :
Mohammad Sajjad Shahbazifar
1
,
Reza Kazemi Matin
2
,
Mohsen Khounsiavash
3
,
Fereshteh Koushki
4
1 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin,
2 - Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
4 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
الکلمات المفتاحية: Group Efficiency, Banking Groups, Machine Learning, Neural Network, Data Envelopment Analysis, Ranking,
ملخص المقالة :
This paper explores the intersection of Group Ranking in Data Envelopment Analysis (DEA) and the potent capabilities of Machine Learning (ML) within the insurance sector, aiming to redefine group efficiency assessment. While DEA has been a cornerstone for evaluating Decision-Making Units (DMUs), the traditional models fall short in the nuanced insurance sector. To address these limitations, ML is integrated into DEA, enabling more effective DMU ranking. The study includes an empirical application within the banking industry, emphasizing the methodology's relevance and potential to transform the insurance landscape.
1. Farrell, M.J., The measurement of productive efficiency. Journal of the royal statistical society: series A (General), 1957. 120(3): p. 253-281.
2. Charnes, A., W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision-making units. European journal of operational research, 1979. 3(4): p. 339.
3. Zhu, N., C. Zhu, and A. Emrouznejad, A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering, 2021. 6(4): p. 435-448.
4. Adler, N., L. Friedman, and Z. Sinuany-Stern, Review of ranking methods in the data envelopment analysis context. European journal of operational research, 2002. 140(2): p. 249-265.
5. Chen, Y., Ranking efficient units in DEA. Omega, 2004. 32(3): p. 213-219.
6. Sexton, T.R., R.H. Silkman, and A.J. Hogan, Data envelopment analysis: Critique and extensions. New directions for program evaluation, 1986. 1986(32): p. 73-105.
7. Liu, H.-h., Y.-y. Song, and G.-l. Yang, Cross-efficiency evaluation in data envelopment analysis based on prospect theory. European Journal of Operational Research, 2019. 273(1): p. 364-375.
8. Doyle, J. and R. Green, Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the operational research society, 1994. 45: p. 567-578.
9. Andersen, P. and N.C. Petersen, A procedure for ranking efficient units in data envelopment analysis. Management science, 1993. 39(10): p. 1261-1264.
10. Mehrabian, S., M.R. Alirezaee, and G.R. Jahanshahloo, A complete efficiency ranking of decision making units in data envelopment analysis. Computational optimization and applications, 1999. 14(2): p. 261-266.
11. Memariani, A. and G. Jahanshahloo, A model for ranking decision making units in data envelopment analysis. Ricerca Operativa, 2001(2001/97).
12. Ang, S., M. Chen, and F. Yang, Group cross-efficiency evaluation in data envelopment analysis: An application to Taiwan hotels. Computers & Industrial Engineering, 2018. 125: p. 190-199.
13. Shahbazifar, M.S., et al., Group ranking of two-stage production units in network data envelopment analysis. RAIRO-Operations Research, 2021. 55(3): p. 1825-1840.
14. Brunetti, A., et al., Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 2018. 300: p. 17-33.
15. Emrouznejad, A. and E. Shale, A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 2009. 56(1): p. 249-254.
16. Misiunas, N., et al., DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status. Omega, 2016. 58: p. 46-54.
17. Thaker, K., et al., A DEA and random forest regression approach to studying bank efficiency and corporate governance. Journal of the Operational Research Society, 2022. 73(6): p. 1258-1277.
18. Barros, C.P. and P. Wanke, Insurance companies in Mozambique: A two-stage DEA and neural networks on efficiency and capacity slacks. Applied Economics, 2014. 46(29): p. 3591-3600.
19. Kwon, H.-B. and J. Lee, Two-stage production modeling of large US banks: A DEA-neural network approach. Expert Systems with Applications, 2015. 42(19): p. 6758-6766.
20. Hebb, D.O., The organization of behavior: A neuropsychological theory. 2005: Psychology press.
21. Stuart, R. and N. Peter, Artificial intelligence: a modern approach. 1995, Prentice-Hall.
22. Mehryar, M., R. Afshin, and T. Ameet, Foundations of Machine Learning. Adaptive computation and machine learning. 2012, MIT Press.
23. McCulloch, W.S. and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943. 5: p. 115-133.
24. Cheng, C.-S., A multi-layer neural network model for detecting changes in the process mean. Computers & Industrial Engineering, 1995. 28(1): p. 51-61.
25. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. nature, 1986. 323(6088): p. 533-536.
