Measuring, Benchmarking and Ranking the Performance of Indian Public Sector Banks

Objectives: The present paper aspires to measure the performance, benchmark and rank various public sector banks using statistical as well as mathematical tools. The present study also uses Sensitivity analysis in DEA to know the stability and instability of efficient DMUs.

Design Methodology: Data are assemble from secondary sources for the year 2015-16 and these data are examine by using Multiple Regression analysis, Spearman Rank correlation test, Data Envelopment analysis, DEA Super efficiency and Sensitivity Analysis. In resent paper various inputs and outputs criteria are taken.

Findings: Multiple Regression analysis result shows for same year, also result fluctuates, when changing the output. Result of Spearman’s Rank correlation test shows that there is no correlation between different ranks when taking different set of output.Result of DEA under CRS and VRS model 6 and 10 banks are with efficiency score 1. To get most efficient bank DEA super-efficiency analysis is used, result indicates State Bank of Patiala is appeared as super efficient in both models. Sensitivity result shows in both model (CRS and VRS) after deleting input and output one after another in some cases efficient DMUs maintain its stability and in some case it became unstable.

Implication/Conclusion: The study provides valuable information on performance measurement, benchmarking, and ranking of public sector banks. The study provide information dealing with valuable factors need to be focused and pay attention to enhance the performance. The study concludes that MCDM technique is a more powerful technique to assess the performance while taking multiple inputs and outputs data simultaneously.