Artificial Intelligence Adoption, Algorithmic Risk and Financial Performance in Indian Banking: A Literature Review

Banks are increasingly turning to artificial intelligence (AI) to carry out a range of tasks, including credit evaluation, fraud detection, customer service and risk management. Although adopting AI may enhance a bank's efficiency and decision-making abilities, it also brings a number of risks, such as algorithmic bias, inaccurate predictions, data quality problems and a lack of transparency. These risks give rise to important questions about how AI adoption relates to financial performance in the banking sector, especially in India, where public-sector and private-sector banks may differ considerably in their technological infrastructure and organizational practices. Drawing on 20 studies identified in a preliminary literature matrix, this review synthesizes the literature around four themes: AI adoption in banking, algorithmic risk and bias, explainable AI and governance, and the relationship between AI adoption and financial performance. The literature reviewed shows that AI applications are widely discussed in the areas of credit scoring, fraud detection and customer services. Yet the benefits of AI cannot be assumed to translate automatically into greater profitability, and predictive accuracy on its own does not ensure fair or transparent decisions. The review points to a potential research gap in the integrated examination of AI adoption, algorithmic risk and financial performance across public- and private-sector Indian banks. Additional empirical research is required to explore these relationships using consistent measures of AI adoption, risk governance and financial performance.