Artificial Intelligence Adoption and Financial Performance in Indian Banks: A Qualitative Content Analysis
DOI: https://doi.org/10.5281/zenodo.23190681
Dr Radhika Uttam, Prof. Navarun Bhattacharjee, Niharika Singh
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Keywords:
Artificial Intelligence, Banking Industry, Financial Results, Corporate Disclosure, Qualitative Content Analysis.
Abstract:
Artificial Intelligence (AI) has emerged as a strategic driver of financial performance in the banking sector by enhancing operational efficiency, improving decision-making, and enabling superior customer service. This research undertakes a qualitative content analysis of four-year report disclosures of four scheduled commercial banks in India between FY2019 and FY2024 comprising 24 documents in the corpus (two public sector banks (State Bank of India and Bank of Baroda) and two private sector banks (HDFC Bank and ICICI Bank). The organized thematic codebook of the study is built around five themes: profitability claims, operational efficiency claims, asset quality claims, disclosed AI applications, narrative tone and specificity. It is based on an interpretivist epistemological stance. The data is analyzed in a two-stage approach that involves a combination of inductive theming to find emergent patterns and deductive coding borrowed from the body of existing literature. Three analytical sub-questions explore the COVID-19 epidemic as a turning point in narratives, the type of AI application with the most often positive or negative results on financial performance, and the disparity in specificity and depth of disclosure between the public and private sector institutions. The findings indicate how AI-related financial success stories have evolved to be aspirational and unclear as of FY2019, more operational and metrics-driven by FY2024. The research adds to the literature by providing a disclosure-based, qualitatively based comprehension of AI adoption as strategically positioned in the banking sector of India that can be used by investors, regulators, and banking executives to determine the accuracy and thoroughness of AI-related corporate messages.