Computing the Efficiency of Bank Branches with Financial Indexes, an Application of Data Envelopment Analysis (DEA) and Big Data
محورهای موضوعی : Statistical Methods in Financial Management
Fahimeh Jabbari-Moghadam
1
,
Farhad Hosseinzadeh Lotfi
2
,
Mohsen Rostamy-Malkhalifeh
3
,
Masoud Sanei
4
,
Bijan Rahmani-Parchkolaei
5
1 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran
3 - Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
5 - Department of Mathematics, Nour Branch, Islamic Azad University, Nour, Iran
کلید واژه: Data Envelopment Analysis (DEA), Clustering, Data Mining and Big Data, Efficiency (Performance),
چکیده مقاله :
In traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and …. may not prove to be economical. The key objective of the proposed algorithm is to segregate the efficient units from that of the other units. In order to gain access to this objective, effectual indexes were created; and taken to assist, in regards the DEA concepts and the type of business (under study), to survey the indexes, which were relatively operative. Subsequently, with the help of one of the clustering techniques and the ‘concept of dominance’, the efficient units were absolved from the inefficient ones and a DEA model was developed from an aggregate of the efficient units. By eliminating the inefficient units, the number of units which played a role in the construction of a DEA model, diminished. As a result, the speed of the computational process of the scores related to the efficient units increased. The algorithm designed to measure the various branches of one of the mercantile banks of Iran with financial indexes was implemented; resulting in the fact that, the algorithm has the capacity of gaining expansion towards big data.
In traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and …. may not prove to be economical. The key objective of the proposed algorithm is to segregate the efficient units from that of the other units. In order to gain access to this objective, effectual indexes were created; and taken to assist, in regards the DEA concepts and the type of business (under study), to survey the indexes, which were relatively operative. Subsequently, with the help of one of the clustering techniques and the ‘concept of dominance’, the efficient units were absolved from the inefficient ones and a DEA model was developed from an aggregate of the efficient units. By eliminating the inefficient units, the number of units which played a role in the construction of a DEA model, diminished. As a result, the speed of the computational process of the scores related to the efficient units increased. The algorithm designed to measure the various branches of one of the mercantile banks of Iran with financial indexes was implemented; resulting in the fact that, the algorithm has the capacity of gaining expansion towards big data.
[1] Sherman, H.D. and Gold, F., Bank Branch Operating Efficiency. Evaluation with Data Envelopment Analysis, Journal of Banking and Finance, 1985; 9, 297-315. Doi:10.1016/0378-4266(85)90025-1
[2] Charnes, A., Cooper, W. W., & 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
[3] Banker, R. D., Charnes, A., & Cooper, W. W., 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
[4] Cook, W. D., & Seiford, L. M., Data envelopment analysis (DEA)–Thirty years on. European journal of operational research, 2009; 192(1), 1-17. Doi: 10.1016/j.ejor.2008.01.032
[5] Dulá, J. H., & Helgason, R. V., A new procedure for identifying the frame of the convex hull of a finite collection of points in multidimensional space, European Journal of Operational Research, 1996; 92(2), 352-367. Doi:10.1016/0377-2217(94)00366-1
[6] Dulá, J. H., Helgason, R. V., & Venugopal, N., An algorithm for identifying the frame of a pointed finite conical hul, Informas Journal on Computing, 1998; 10(3): 323-330. Doi:10.1287/ijoc.10.3.323
[7] Barr, R. S., & Durchholz, M. L, Parallel and hierarchical decomposition approaches for solving large-scale data envelopment analysis models, Annals of Operations Research, 1997; 73, 339-372. Doi:10.1023/a:1018941531019
[8] Sueyoshi, T., & Chang, Y. L., Efficient algorithm for additive and multiplicative models in data envelopment analysis, Operations Research Letters, 1989; 8(4), 205-213. Doi:10.1016/0167-6377(89)90062-x.
[9] Dulá, J. H., & Thrall, R. MA., computational framework for accelerating DEA, Journal of Productivity Analysis, 2001; 16(1), 63-78. Doi:10.1023/a:1011103303616
[10] Chen, W. C., & Cho, W. JA., procedure for large-scale DEA computations, Computers & Operations Research, 2009; 36(6), 1813-1824. Doi:10.1016/j.cor.2008.05.006
[11] Dulá, J. H., & López, F. J., DEA with streaming data, Omega, 2013; 41(1), 41-47.
Doi: 10.1016/j.omega.2011.07.010
[12] Chen, W. C., & Lai, S. Y., Determining radial efficiency with a large data set by solving small-size linear programs, Annals of Operations Research, 2017; 250(1), 147-166. Doi: 10.1007/s10479-015-1968-4.
[13] Zhu, Q., Wu, J., & Song, M., Efficiency evaluation based on data envelopment analysis in the big data context, Computers & Operations Research, 2018; 98, 291-300. Doi: 10.1016/j.cor.2017.06.017
[14] Khezrimotlagh, D., Zhu, J., Cook, W. D., & Toloo, M., Data envelopment analysis and big data, European Journal of Operational Research, 2019; 274(3), 1047-1054. Doi: 10.1016/j.ejor.2018.10.044.
[15] Dellnitz, A Big data efficiency analysis: Improved algorithms for data envelopment analysis involving large datasets, Computers & Operations Research, 2022; Volume 137,2022,105553.
Doi: 10.1016/j.cor.2021.105553.
[16] Vijayarani, S., & Nithya, S. An efficient clustering algorithm for outlier detection, International Journal of Computer Applications, 2011; 32(7), 22-27.
[17] Vijayarani, S., & Nithya, S., Sensitive Outlier Protection in Privacy Preserving Data Mining, International Journal of Computer Applications, 2011; 33(3).
