Evaluation of information technology impact on bank’s performance: The Ghanaian experience

Appiahene, Peter; Missah, Yaw Marfo; Najim, Ussiph

doi:10.1177/1847979019835337

Cited by 31 publications

(23 citation statements)

References 47 publications

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“…Overall, for the result of this research about how IT affects the bank efficiency confirms other previous researches from (Ahmadirezaei, 2011;Dangolani, 2011) which explain through IT can reduce operational cost. Moreover, this research also goes along with (Appiahene et al, 2019;Romdhane, 2013) which suggest IT had significant impact on bank efficiency but not with (Ho & Mallick, 2010) findings which reflect negative relation between adopting IT and bank efficiency. As adopting the IT caused high efficiency, SB in this revolution industry 4.0 era appears to be more competitive (Gichungu & Oloko, 2015) and generate maximum profit (Yang & Qi, 2017).…”

Section: Resultsmentioning

confidence: 47%

Analysis of Sharia Banking Efficiency in Industry Revolution 4.0 Era

Kaban

Setyawati

2020

IJSSB

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This research aimed to analyze the efficiency of Sharia Banking in Indonesia for the period of 2013-201, the era of industry revolution 4.0. The research method used Data Envelopment Analysis while the data was taken from the annual report of Sharia Banking Statistic issued by Indonesia Financial Services Authority for the intended years. The input variables were the third-party fund, labor cost, and other cost while the output variables were total financings and operating income. Both with CRS and VRS model input approach, Sharia Banking in Indonesia generally already had high-efficiency value either based on years or banks. The banks who had not been in high-efficiency criteria were highly recommended learning from the benchmark banks. The result showed in general the use of technology and the internet by Sharia Banking was already appropriate, which caused their operations running efficiently.

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Section: Resultsmentioning

confidence: 47%

Analysis of Sharia Banking Efficiency in Industry Revolution 4.0 Era

Kaban

Setyawati

2020

IJSSB

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“…For the machine learning category, Rebai et al (2020) proposed a machine-learning approach to predict the productivity of secondary schools, using DEA in the first stage and machine-learning techniques in the second stage to map variables associated with higher performances. Likewise, Appiahene et al (2019) combined DEA with machine learning algorithms. They studied the impact of information technology (IT) productivity on bank efficiencies and verified that information technology has a significant impact on bank performances, with the fact that almost 80% IT efficient banks are overall efficient.…”

Section: Research Areas and Methodologiesmentioning

confidence: 99%

Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

Shi

Zhu

Charles

2021

Journal of the Operational Research Society

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This paper provides a comprehensive review of the applications of data science techniques and methodologies in productivity. The paper is structured as a combination of a bibliometric analysis and an empirical review. In the bibliometric analysis, the sources, authorship, and documents are reviewed and discussed. Visualisation aids, including summative tables and figures, are incorporated. In the empirical review, the corpus of 533 articles identified are reviewed based on the application areas of data science approaches and the primary methodology of the papers, and the selected most impactful and relevant papers in each methodological category are discussed in detail. The objective of this paper is to provide an overview of the current predominant trends and patterns in data science and productivity, explore how the interplay has been manifested, and provide an outlook on future research orientations.

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“…Data Envelopment Analysis is a nonparametric method that produces a comparative ratio of weighted outputs to inputs for each Decision Making Unit (DMU) under consideration [31][32][33]. is study presumes that there are n DMUs to be evaluated and in this case (n � 444).…”

Section: Basics Of Data Envelopment Analysis (Dea)mentioning

confidence: 99%

“…e proposed dual role DEA model adapted from[33].Advances in Fuzzy SystemsDeposit Stage, I Input:Fixed Assets (billions of GH) denoted as A Total IT expenditure (billions of GH) denoted as I Total number of Employees denoted as the stage I denoted as Do e deposit also denoted as D.Output:Percentageof Performing loans (PL) Profit accrued from investing in securities (R) Overall Stage Input Efficiency of stage 2 denoted as No Fixed Assets (billions of GH) denoted as A Total IT expenditure (billions of GH) denoted as I Total number of Employees denoted as E Output: Percentage of Performing Loans (PL) which is equal to the percentage of nonperforming loans and 100% Profit accrued from investing in securities (R) After using the classical CCR model of DEA, the efficiency score for each DUM in each stage w resulted in the following three types of efficiency score: Efficiency of stage I, Do Efficiency of stage II, No Overall efficiency, G 4.3. Dataset and Sample.…”

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confidence: 99%

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Appiahene

Missah

Najim

2020

Advances in Fuzzy Systems

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The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

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Evaluation of information technology impact on bank’s performance: The Ghanaian experience

Cited by 31 publications

References 47 publications

Analysis of Sharia Banking Efficiency in Industry Revolution 4.0 Era

Analysis of Sharia Banking Efficiency in Industry Revolution 4.0 Era

Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

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