 Transforming Indian Banking: The Impact of Intelligent Systems and Process Automation on Financial InnovationTiyas Sarkar and Manik Rakhra Chapter 7 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 145-165 from World Scientific Publishing Co. Pte. Ltd. Abstract: In this study, we analyze data from 15 banks to capture the full spectrum of customer attitudes, from very optimistic to highly conservative. The State Bank of India and Punjab National Bank say they will benefit more, while others like Dhanlaxmi Bank are more cautious. This dichotomy is a strong indication of the determinants that influence the level of AI readiness within an institution, including but not limited to the firm’s culture, technological endowment, and customer base. These findings are further complemented by the results of the chi-square test, which elucidate the inconsistency of opinions among financial institutions regarding the impact of AI and automation. These differences give rise to the need for strategic diversity that is suited to the particular characteristics of each bank. While banks continue to undergo AI-mediated transformations, it will be important to deal with the specific issues and expectations posed by different institutional contexts in order to facilitate acceptance and advancement. Additionally, considering the views of banking experts from different positions, there is a wide range of viewpoints and a clear need for approaches that are suited to the specific roles within different banks. Incorporating AI and automation into the operating processes of banks is a complex issue and requires a proper understanding of the unique setting of every company. Keywords: Artificial Intelligence; AI in Finance; Financial Technology; FinTech; Machine Learning; Deep Learning; Neural Networks; Automation; Robotics; Intelligent Automation; Algorithmic Trading; Robo-Advisors; Predictive Analytics; Data Science; Big Data; Risk Management; Credit Scoring; Fraud Detection; AI Ethics; Responsible AI; AI Governance; Regulatory Compliance; Financial Regulations; Cybersecurity; Blockchain; Cryptocurrencies; Bitcoin; Ethereum; InsurTech; Digital Banking; AI in Banking; AI in Investments; AI in Insurance; AI in Wealth Management; AI in Payments; Natural Language Processing; AI Chatbots; Virtual Assistants; Customer Experience; Personalization; Sentiment Analysis; Credit Risk Modeling; Financial Forecasting; AI-powered Decision Making; Quantitative Finance; Trading Algorithms; High-Frequency Trading; AI in Hedge Funds; AI-driven Market Analysis; Automated Financial Services; Smart Contracts; Digital Assets; AI-driven Portfolio Management; Financial Planning; AI in Asset Management; AI in Lending; AI in Mortgage Industry; Financial Inclusion; Alternative Data; Explainable AI; Model Interpretability; AI and Human Collaboration; AI-driven Credit Analysis; Robo-Trading; Supervised Learning; Unsupervised Learning; Reinforcement Learning; AI-driven Customer Insights; Data-driven Decision Making; Financial Market Predictions; Behavioral Finance; Smart Finance; AI-based Anomaly Detection; Computational Finance; Financial Data Analytics; Financial Fraud Prevention; Future of Work in Finance; AI Strategy in Financial Firms; Financial Risk Analytics; Digital Transformation in Finance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800618428_0007 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd. |
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