English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The banking industry is very important for an economic cycle of each countryand provides some quality of services for us. With the advancement intechnology and rapidly increasing of the complexity of the businessenvironment, it has become more competitive than the past so that efficiencyanalysis in the banking industry attracts much attention in recent years. Frommany aspects, such analyses at the branch level are more desirable. Evaluatingthe branch performance with the purpose of eliminating deficiency can be acrucial issue for branch managers to measure branch efficiency. This work notonly can lead to a better understanding of bank branch performance but alsogive further information to enhance managerial decisions to recognizeproblematic areas. To achieve this purpose, this study presents an integratedapproach based on Data Envelopment Analysis (DEA), Clustering algorithms andPolynomial Pattern Classifier for constructing a classifier to identify a classof bank branches. First, the efficiency estimates of individual branches areevaluated by using the DEA approach. Next, when the range and number of classeswere identified by experts, the number of clusters is identified by anagglomerative hierarchical clustering algorithm based on some statisticalmethods. Next, we divide our raw data into k clusters By means ofself-organizing map (SOM) neural networks. Finally, all clusters are fed intothe reduced multivariate polynomial model to predict the classes of data.

Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches