Computing the Efficiency of Bank Branches with Financial Indexes, an Application of Data Envelopment Analysis (DEA) and Big Data

Subject Areas : Statistical Methods in Financial Management

Fahimeh  Jabbari-Moghadam ¹ (Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Farhad  Hosseinzadeh Lotfi ² (Department of Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran)
Mohsen  Rostamy-Malkhalifeh ³ (Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Masoud  Sanei ⁴ (Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran )
Bijan  Rahmani-Parchkolaei ⁵ (Department of Mathematics, Nour Branch, Islamic Azad University, Nour, Iran )

Received: 2022-05-28 Accepted : 2023-01-01 Published : 2024-04-27

Keywords: Data Envelopment Analysis (DEA), Clustering, Data Mining and Big Data, Efficiency (Performance),

Abstract :

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 When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and ….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.

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