2025, Vol. 6, Special Issue 2
A comparative evaluation of HDFC Bank's financial stability in the context of AI adoption using the CAMELS model
Author(s): Naresh Babu KS, Shreekritha and Meenaz Zaib
Abstract: By improving customer service, risk management, and operational efficiency, artificial intelligence (AI) is rapidly changing the banking industry. Leading this technical change is HDFC Bank, one of the biggest private sector banks in India. The CAMELS methodology, which assesses capital adequacy, asset quality, management effectiveness, earnings, liquidity, and sensitivity to market risk, is used in this study to compare HDFC Bank's financial performance before and after the financial institution adopted AI. The study is based on secondary data that spans a decade, encompassing five years prior to the adoption of AI (FY 2012–2016), one year following adoption (2017), and five years following adoption (FY 2018–2022). Financial ratios were examined under each CAMELS parameter, and the significance of the observed changes was tested using SPSS's paired sample t-test. Results show that after AI deployment, financial metrics generally improved, especially in areas like capital adequacy, profits, and management effectiveness. The fact that the benefits are not statistically significant, according to hypothesis testing, suggests that AI has not yet been completely incorporated into all aspects of banking. According to the study's findings, HDFC Bank needs to engage in staff upskilling, embrace a more thorough AI strategy, and extend its use throughout core processes in order to fully realize AI's promise. The financial impact of AI in Indian banking is better understood thanks to this article, which also provides insights for future study and industry strategy.
DOI: 10.22271/27084515.2025.v6.i2Sc.701
Pages: 237-244 | Views: 125 | Downloads: 38
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How to cite this article:
Naresh Babu KS, Shreekritha, Meenaz Zaib. A comparative evaluation of HDFC Bank's financial stability in the context of AI adoption using the CAMELS model. Asian J Manage Commerce 2025;6(2S):237-244. DOI: 10.22271/27084515.2025.v6.i2Sc.701
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