This study attempts to evaluate the financial performance of ten selected Indian commercial banks from 2018 to 2023 and compares the rankings produced by prominent distance-based multi-criteria decision-making (MCDM) methods.
This study employs the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to determine the weights of the indicators under the CAMELS framework. The Comprehensive Distance-Based Ranking (COBRA) method is then used to evaluate and rank the selected. The robustness of the findings is assessed by comparing the results with four distance-based MCDM methods, i.e., the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Evaluation Based on Distance from Average Solution (EDAS), Combinative Distance-Based Assessment (CODAS) and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Finally, the Friedman test, Kendall's coefficient of concordance (W) and the Bonferroni-adjusted Wilcoxon post hoc test are applied to statistically validate the results.
HDFC Bank Ltd. and Kotak Mahindra Bank Ltd., among the top performers during 2018–2022, experienced a notable decline in 2023, whereas Axis Bank Ltd. improved steadily. Friedman and Bonferroni-adjusted Wilcoxon tests revealed significant differences between COBRA and other distance-based methods. COBRA generated the most consistent rankings, while TOPSIS and EDAS were more sensitive in distinguishing alternatives. CRITIC weights indicated temporal fluctuations in CAMELS indicators, with profitability, asset quality and employee productivity emerging as important performance drivers. These findings emphasize the need for adaptive performance management systems that reflect evolving priorities and emerging areas requiring managerial attention.
Although the analysis aimed to follow the guidelines and principles related to MCDM decision analysis and theories involved in bank performance evaluation, this study has some limitations. It is limited to six consecutive years, from 2018 to 2023. The study could be expanded, and the impact of different mergers and acquisitions on the Indian banking sector's performance can be measured using the proposed approach.
The findings provide policy implications for regulators, such as the Reserve Bank of India. The movement of weights and rank shifts identified over time emphasize the necessity of consistent adjustment in supervisory priorities, especially for asset quality, provisioning for NPAs and employee efficiency. Second, the study also emphasizes the necessity of including both profitability ratios and risk sensitivity measures in policy-making for the establishment of systemic resilience.
For practitioners such as bank managers and analysts, this study offers a replicable decision-support model that is quantitative in rigor yet flexible. The COBRA model, as validated through robustness analysis combined with other MCDM methods, can be utilized as a strategic benchmarking tool. Investors can utilize the ranking for decision-making purposes, while auditors and credit rating agencies can consider using this method to complement their risk assessment processes. In all, the research not only fills a methodological lacuna in bank performance measurement literature but also offers a useful toolkit for tracking and enhancing banking efficiency in an ever-changing economic environment.
The study contributes to MCDM and performance management research by comparing distance-based MCDM methods and validating an integrated CRITIC–COBRA approach within the CAMELS framework for banking performance evaluation.
