Revisiting the Efficiency of Indian Banking Sector: An Analysis of Comparative Models Through Data Envelopment Analysis

Tanwar, Jyoti; Seth, Himanshu; Vaish, Arun Kumar; Rao, Narendar V.

doi:10.46281/ijfb.v4i1.585

Cited by 15 publications

(13 citation statements)

References 48 publications

(39 reference statements)

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“…This result suggests that public banks are more profitable than private banks when considering their NIMs. This confirms the results previously found by Chaffai and Dietsch (1998), Smida and Ayadi (2006), Anwar and al. (2020).…”

Section: The Sensitivity Of the Net Interest Margin To It Investmentsupporting

confidence: 93%

Impact of Information Technologies’ Investments on the Profitability of Tunisian Banks: Panel Data Analysis

Romdhane¹

2021

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This study examines the relationship between Information Technology investment and the profitability of Tunisian banks, via static and dynamic panel regression models. Our study focused on 15 Tunisian banks for 19 years (2001-2019). To assess the profitability of these banks, three measures were used: two traditional accounting ratios and net interest margin. Our research has shown the importance of the role played by IT in Tunisian banks since IT investments improve their profitability. This finding contradicts the “Productivity Paradox” that high IT investments are not associated with better performance. Indeed, Tunisian banks are acting on their size to boost their performance, and the more the banks take the risk by granting more loans, the more profitable they are by increasing their Return on Assets. Finally, public banks are more profitable than private banks when considering their net interest margin. JEL Classification Codes: B21, C58, G21, G32, O32.

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Section: The Sensitivity Of the Net Interest Margin To It Investmentsupporting

confidence: 93%

Impact of Information Technologies’ Investments on the Profitability of Tunisian Banks: Panel Data Analysis

Romdhane¹

2021

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“…A study was conducted on the Indian banking industry to evaluate bank efficiency through Data Envelopment Analysis (DEA) method to compare the efficiency of the public, private, and foreign banks. The result of this study found that public banks are more efficient than private and foreign banks in India (Tanwar et al, 2020). Besides, Bank efficiency also varies with the performance of Non-Performing Loans of banks.…”

Section: Overview Of the Literaturementioning

confidence: 68%

Predicting the Efficiency of Commercial Banks in Bangladesh

Solaiman¹,

Chowdhury

Hossain

et al. 2021

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This paper examines the efficiency of the thirty-six commercial banks (27 Domestic and 9 Foreign Banks) by reviewing Literature and analyzing 6 years (2011-2016) data. The sample was selected based on the availability of data. It is assumed that the banking sector complies with the variable returns to scale (VRS) approach which means the output of a bank is not proportionately related to its inputs. Therefore, VRS in the ‘Data Envelop Analysis (DEA)’ technique has been employed in this paper. The findings reveal that most (30 banks out of 36 banks) of the banks of Bangladesh are inefficient in terms of technical, allocative, and scale efficiency during the 2011-2016 periods. Conversely, only six banks (4 domestic banks and 2 foreign banks) were found efficient in overall scores in this scrutinization. This study did not find any single bank as efficient in all categories (allocative efficiency, technical efficiency, pure efficiency ratio) for the whole study period (2011-2016). This paper provides valuable intuition, analysis, and comments to the managers and policymakers of the bank’s efficiency score so that they comprehend their position. Finally, this paper suggests necessary steps to transform the inefficient banks into efficient banks, and to make stable the banking sector of Bangladesh. JEL Classification Codes: E51, G21, M1.

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“…The bank should decrease its fund in non-income generating assets and reinvest those funds in income-generating assets. The optimum utilization of fixed and current assets can also increase the efficiency of the bank (Tanwar et al, 2020).This model reveals areas that need focus to attain an optimal level. No institution can run at an optimal level until it knows the particular domain where it can improve.…”

Section: Resultsmentioning

confidence: 99%

“…The history of banking in India is as old as Vedic civilization, where usury has been commonly referred to money lenders. Banking in India originated in the 18th century and has evolved over the years to the present shape ( Tanwar, Seth, Vaish, and Rao, 2020). Some of RBI's significant reform events led to greater competition and strengthening of the Indian banking sector.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Asset Liability Management in Banks Using Goal Programming and Ahp

Tanwar¹,

Vaish²,

Rao³

2020

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Asset Liability Management has gained popularity in the banking sector. Earlier banks focused on asset allocation, but now the management of assets and liabilities is equally essential. Asset liability management targets the optimum distribution of funds in assets and managing liabilities so that banks can earn higher profits and minimize risk. In this paper, the optimization of assets and liabilities of Indian banks has been concentrated using mathematical models. Combining the Analytical Hierarchy Process (AHP) and Goal Programming (GP) model has been used to solve the optimization problem. AHP is a multi-criteria decision-making approach for deriving priority weights. Goal Programming is a linear programming model to solve complex issues having multiple objectives. In this paper, the primary data gathered from Bank senior managers have been analyzed using the AHP approach to derive weights for criteria. These weights are assigned to goals in goal programming to prioritize the goals. Secondary data on OBC bank is used in goal programming from 2010-2019 collected from OBC bank's annual reports and RBI websites. The findings show that OBC bank has the scope of improving its assets and liabilities position to increase its profit and minimize the risk. The model generates an optimum balance sheet that achieves the set goals and satisfies all the statutory and planning constraints. The same model can be useful for scheduled commercial banks in India with modifications concerning banks' targets and controls. The model developed in this paper is helpful for bank managers in planning and forecasting. AHP and GP's combined approach is unique in this paper, which uses experts' knowledge and applies it in the model. The model is created on the bank's realistic goals and constraints after carefully considering the issues faced by bank officials. The paper is limited to the Indian Banking system as other countries have different balance sheet structures and constraints.

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Revisiting the Efficiency of Indian Banking Sector: An Analysis of Comparative Models Through Data Envelopment Analysis

Cited by 15 publications

References 48 publications

Impact of Information Technologies’ Investments on the Profitability of Tunisian Banks: Panel Data Analysis

Impact of Information Technologies’ Investments on the Profitability of Tunisian Banks: Panel Data Analysis

Predicting the Efficiency of Commercial Banks in Bangladesh

Mathematical Modeling of Asset Liability Management in Banks Using Goal Programming and Ahp

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