Performance prediction of DMUs using integrated DEA-SVR approach with imprecise data: application on Indian banks

Data Envelopment Analysis (DEA) is a widely used performance analysis tool, but for every newly added unit in a data set, it requires complete re-processing of the DEA model. In real applications, data sets are usually imprecise and enormously increasing, which demands a large computer processing time. A predictive capability of DEA, typically in uncertain environment, can solve this problem. This paper aims to establish an integrated DEA and support vector machine for regression (SVR) approach with imprecise data for efficiency estimation and prediction in optimistic and pessimistic environments. To illustrate the potential of the proposed approach, it is applied on Indian banks for evaluation and prediction of different efficiency measures (technical, pure technical, and scale) during the period 2011-2020. To study the sensitivity analysis and productivity of Indian banks' efficiency with time, DEA window analysis (window width five years) and Malmquist productivity index (MPI) in optimistic and pessimistic environments have been improvised with interval data. The new approach results in very reliable and accurate prediction (mean square error: 2 x 10-3 to 5 x 10-3) of different efficiency measures that benefits bank experts for planning and decision making on bank addition/merger. Other findings conclude that (i) technical efficiency is more influenced by managerial (pure technical) inefficiencies rather than bank size (scale efficiency) in all windows, and (ii) on the contrary to all other banks, Yes Bank Ltd. examined decreased productivity in both the environments, (iii) technical efficiency change has higher impact on MPI in both environments as compared to technological change.