According to the Reserve Bank of India (RBI), Indian banking system as a whole is sound, adequately capitalized and well-regulated. As the credit, market and liquidity risk studies revealed, Indian banks are generally resilient and have withstood the global downturn well. The bouncing back of Indian economy to a positive growth is indeed attributed to the improved performance of the banking industry. But a recent nation-wide survey carried out by the Confederation of Indian Industries-Boston Consultancy Group, however, reveals that the current level of financial inclusion in India is hardly 47% of the total households. It is, of course, an improvement over what was reported—35%—five years ago; nevertheless, more than half of the population in the country is still dependent for its credit needs on non-banking agencies. Non-availability of credit to people living in the margins is certainly preventing them from harnessing/realizing their full potential. This obviously breeds discontent in the society, that too, while a select few sections of the society are growing at a higher rate. No wonder, if it breeds civil disorder even. One argument that currently deters banks from providing credit to sundry borrowers is said to be the cost of delivery. The report also points out that mere incremental changes in business models won't deliver the desired results. Which is why, there is an urgent need for banks to innovate and create low-cost and efficient distribution network by fully deploying technology so that whole of India’s population can enjoy access to banks. Such an access to banks alone can service the credit needs of the growing economy fully. And this in turn will make human capital more productive besides ensuring inclusive growth.

In all this game plan, what matters most is the profitability of the banking system and the soundness of the drivers of profitability across the geography. In this context, the first paper of the issue, “Examining the Performance of Banks in India: Post Transition Period”, evaluates the performance of the Indian banking system post reforms—during 1997 to 2005. The authors have calculated the efficiency measures of the banking system in terms of productive performance, scale elasticity, efficiency and capacity utilization by undertaking Data Envelopment Analysis (DEA). The authors opine that the mean efficiency scores of Indian banks in general and of the bigger banks in particular have improved considerably during the study period affirming that the reforms launched have had a positive impact on the performance of the Indian banking sector. There is however a marked difference between nationalized banks and others as a class in cost efficiency parameters. Keeping in view the findings of the study, the authors opine that efficiencies of the banks may be measured over a period using dynamic cost frontier DEA models, while Malmquist productivity index may be used for analyzing productivity change.

While talking about the efficient functioning of the Indian banking system, we have another paper, “Credit Risk Management of Loan Portfolios by Indian Banks: Some Empirical Evidence”, which assesses how efficiently banks are measuring the creditworthiness of the borrowers so as to select right borrowers to minimize credit risk—the risk of default by the borrower-impacting banks’ profitability adversely. Using discriminant model, the authors have compared the performance of different credit risk management techniques adopted by banks to find out a standard for predicting the defaulters with accuracy. The authors have used data pertaining to 40 Indian banks, which they have divided into two groups of 20 each, to develop a coefficient for discriminant analysis and to develop the model for predicting the defaulters and ultimately the utility of the developed model has been verified using data of five banks for a period of six years. The authors opine that by using the technique developed by them, non-performing assets can be reduced considerably.

Continuing with the concern for the profitability of banks, the next paper of the issue, “Relative Importance of Profitability Drivers of Indian Banks: A Preference Decomposition Approach”, attempts to estimate the relative importance of advances, investments, deposits and other income in predicting profits, for profit generation squarely rests on striking an appropriate balance among the key drivers such as the advances, deposits and other income. In this regard, the authors have undertaken a comparative assessment of Ordinary Least Square (OLS) method and Robust Regression based on Least Absolute Deviation method so as to pick the right one that best fits to predict profit in a given situation. Based on their study, the authors conclude that the robust regression method outperforms the OLS in predictive accuracy. The study, as spelt out by the authors, has two limitations: one, data filtering for inconsistencies warranted exclusion of some banks from the sample and two, the emphasis was more on predictive accuracy rather than hypothesis testing.

Moving on further down the profitability curve of Indian banks, we have another paper, “Benchmarking Performance of Public Sector Banks in India”, which analyzes the performance of public sector banks in India using various accounting ratios such as financial efficiency, operational efficiency, etc., and uses them to build performance index to rank the banks. The authors have used Principal Component Analysis method to build the index and thereby rank the banks. The study revealed that State Bank of India continues to be the No. 1 bank in India, over the study period of ten years, while Punjab National Bank, Canara Bank, Bank of India and Bank of Baroda compete for the second place in different years.

Moving away from profitability per se, we have the last paper, “Service Quality of Indian Bank in Thanjavur District: Evidence from Survey Data”, which deals with the quality of service rendered by a bank which ultimately leads to growth in profitability or otherwise. The authors have made an attempt to assess the impact of various services rendered by Indian Bank in Thanjavur district of Tamil Nadu on the perception of customer about the overall service quality of the bank, which in turn defines the loyalty of the customer. Using the five-factor dimensional models—Tangibles, Reliability, Responsiveness, Assurance and Empathy—and adopting the quality scale proposed by Parasuraman et al., the authors, following a purposive sampling method, obtained the responses of 175 bank customers at various locations of bank branches in Thanjavur district through a constructive questionnaire and analyzed the responses to understand the correlation between the components of service and the quality of the service as perceived by the customer and presented the critical quality dimensions of banking services.

-- GRK Murty
Consulting Editor