Study on Commercial Bank Branches Performance Evaluation Using Self-Adaptive RBFNN and UDM

2010 ◽  
Author(s):  
Diao XiaoHua ◽  
Kang Shiying
Keyword(s):  
Performance Evaluation ◽  
Commercial Bank ◽  
Bank Branches ◽  
Self Adaptive

MULTICRITERIAL RANKING APPROACH FOR EVALUATING BANK BRANCH PERFORMANCE

2004 ◽  
Vol 03 (02) ◽  
pp. 321-335 ◽  
Author(s):  
FUAD ALESKEROV ◽  
HASAN ERSEL ◽  
REHA YOLALAN
Keyword(s):  
Commercial Bank ◽  
Large Scale ◽  
Real Life ◽  
Multiple Criteria ◽  
Ranking Methods ◽  
Bank Branch ◽  
Bank Branches ◽  
Life Data ◽  
Real Life Data

14 ranking methods based on multiple criteria are suggested for evaluating the performance of the bank branches. The methods are explained via an illustrative example, and some of them are applied to a real-life data for 23 retail bank branches in a large-scale private Turkish commercial bank.

scholarly journals A Cloud-based Middleware for Self-Adaptive IoT-Collaboration Services

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4559
Author(s):  
Park ◽  
Park
Keyword(s):  
Performance Evaluation ◽  
Virtual Machine ◽  
Context Awareness ◽  
Efficient Solutions ◽  
Cloud Services ◽  
Single System ◽  
Considerable Time ◽  
Web Based ◽  
Criminal Suspect ◽  
Self Adaptive

The middleware framework for IoT collaboration services should provide efficient solutions to context awareness and uncertainty issues among multiple collaboration domains. However, existing middleware frameworks are mostly limited to a single system, and developing self-adaptive IoT collaboration services using existing frameworks requires developers to take considerable time and effort. Furthermore, the developed IoT collaboration services are often dependent on a particular domain, which cannot easily be referenced in other domains. This paper proposes a cloud-based middleware framework that provides a set of cloud services for self-adaptive IoT collaboration services. The proposed middleware framework is generic in the sense that it clearly separates domain-dependent components from the layers that leverage existing middleware frameworks. In addition, the proposed framework allows developers to upload domain-dependent components onto the cloud, search for registered components, and launch Virtual Machine (VM) running a new MAPE cycle via a convenient web-based interface. The feasibility of the proposed framework has been shown with a simulation of an IoT collaboration service that traces a criminal suspect. The performance evaluation shows that the proposed middleware framework runs with an overhead of only 6% compared to pure Java-based middleware and is scalable as the number of VMs increases up to 16.

A Movement Trend Based Self-Adaptive Vertical Handoff Algorithm and Its Performance Evaluation

2009 ◽  
Vol 31 (1) ◽  
pp. 112-119
Author(s):  
Min LIU ◽  
Zhong-Cheng LI ◽  
Xiao-Bing GUO ◽  
Kun ZHENG
Keyword(s):  
Performance Evaluation ◽  
Vertical Handoff ◽  
Self Adaptive

scholarly journals Internal performance evaluation: the case of bank branches

2008 ◽  
Vol 19 (3) ◽  
pp. 302-324 ◽  
Author(s):  
E. Grifell‐Tatjé ◽  
P. Marques‐Gou
Keyword(s):  
Performance Evaluation ◽  
Bank Branches

Performance Evaluation and Field Trial of Self-Adaptive Microgel Flooding Technology

2018 ◽  
Author(s):  
Zhe Sun ◽  
Xiaodong Kang ◽  
Xiaoxu Tang ◽  
Xingcai Wu ◽  
Qiang Li ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Field Trial ◽  
Self Adaptive

scholarly journals Relationship Between Commercial Bank Availability and Income Inequality

2021 ◽  
Vol 12 (3) ◽  
pp. 156-162
Author(s):  
Aman Takiyar ◽  
Varun Chotia
Keyword(s):  
Income Inequality ◽  
Commercial Bank ◽  
Financial Institutions ◽  
Positive Relationship ◽  
Estimation Methods ◽  
Financial Network ◽  
Bank Branches ◽  
Panel Data Estimation ◽  
The Relationship ◽  
Data Estimation

The objective of this study is to examine the relationship between commercial bank branches availability and income inequality. Further, the study also assesses the interaction effect of corruption and commercial bank availability on income inequality. The present study uses panel data estimation methods for analysing the above relationship for SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka). The analysis suggests that a positive relationship exists between income inequality and financial availability in the initial stages. However, as the financial institutions reach a level of maturity and more people are integrated in the financial network, the level of income inequality starts reducing. Moreover, increase in financial availability helps in reducing income inequality when it is supported by less corrupt institutions. Policymakers should focus on reducing the level of corruption so as to enhance the effectiveness of the penetration of commercial bank branches.

scholarly journals A Machine Learning Approach to Evaluate the Performance of Rural Bank

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jun Wei ◽  
Tao Ye ◽  
Zhe Zhang
Keyword(s):  
Performance Evaluation ◽  
Commercial Bank ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Bank Performance ◽  
Reduction Step ◽  
Step Size ◽  
Tree Model ◽  
Tree Algorithm ◽  
Original Algorithm

In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks’ performance in order to better evaluate their performance.

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