International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 5

• Author(s)Vinod Kumar Saxena, Ashutosh Deshpande, Fatima Begum Siddiqui
• AffiliationDepartment of Finance and Accounting, Durgadevi Institute of Management and Commerce, Varanasi, Uttar Pradesh, India Department of Commerce, Millennium College of Arts, Commerce and Management, Bhopal, Madhya Pradesh, India
• Page No.60-66
• Volume, Issue & YearVolume 3, Issue 5, May 2026
• Published On2026/05/12
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350
• DOIhttps://doi.org/10.5281/zenodo.20353235

Abstract

The Indian banking sector has undergone a profound structural transformation in the period 2018–2023, shaped by the Insolvency and Bankruptcy Code (IBC) 2016 operationalisation, the Reserve Bank of India's (RBI) Asset Quality Review (AQR) and Prompt Corrective Action (PCA) framework, and the consequential large-scale resolution of stressed assets in public sector banks (PSBs). Gross Non-Performing Asset (GNPA) ratios for PSBs declined from a peak of 14.6% in 2018 to 4.8% in 2023 — a historically unprecedented balance sheet cleansing — while the introduction of Ind AS (Indian Accounting Standards) aligned provisioning norms, the recapitalisation of PSBs through ₹3.10 lakh crore in government capital infusion, and the RBI's Basel III Capital Adequacy framework implementation have collectively reshaped the capital and profitability landscape. Yet systematic longitudinal and cross-sectional analysis comparing financial performance across public sector, private sector, and foreign bank categories using panel data methods remains limited for the post-IBC period. This study analyses a balanced panel dataset of 40 Scheduled Commercial Banks (18 PSBs, 14 private sector banks, 8 foreign banks) over the six-year period 2018–2023, using pooled OLS, fixed-effects (FE), and random-effects (RE) panel regression models to examine the determinants of bank profitability (Return on Assets and Return on Equity) and technical efficiency (Data Envelopment Analysis). Variables examined include Gross NPA ratio, Capital Adequacy Ratio (CRAR), Loan-to-Deposit ratio, Operating Expense ratio, Net Interest Margin, and digital adoption index. Results confirm that GNPA ratio is the strongest negative predictor of ROA (β = −0.52) and ROE (β = −0.48), while CRAR is a significant positive predictor of both. DEA technical efficiency scores reveal that foreign banks (mean = 0.90) and private sector banks (mean = 0.84) significantly outperform PSBs (mean = 0.72). A novel finding is that digital adoption index is significantly and negatively correlated with cost-income ratio (r = −0.81, p < 0.01), providing the first multi-bank empirical evidence of digitalisation-driven operational efficiency gains in the Indian context. Policy implications for RBI's ongoing supervisory framework and the government's PSB consolidation strategy are discussed

Keywords: non-performing assets, capital adequacy, return on assets, bank profitability, IBC 2016, DEA efficiency, panel data, public sector banks, digital banking, India

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