Book Title: Advanced Studies in Multidisciplinary Research and Innovation (ASMRI)
Chief Editors: Dr. Jagdish Kumar Sahu and Dr. Krishna Ashutoshbhai Vyas
Associate Editors: Dr. N. Ramesh Chandra Srikanth and Dr. Lourdu Vesna J
Co-Editors: Dr. Aarti Sharma and Dr. Pushpa Mamoria
ISBN: 978-93-7183-010-2
Chapter: 10
DOI: https://doi.org/10.59646/745/10
Author: Dr. Sunil Adhav
Abstract
Artificial Intelligence (AI) is revolutionizing the contemporary banking landscape, impacting methods of financial services provision, management and security. The current research paper examines the topic of using AI in the banking sector, taking a look at some of the most popular applications of AI, which include fraud detection, credit scoring, customer service automation, risk management, and algorithmic trading. This research is done through a mixed-methods approach, using quantitative data from banking performance reports and digital transaction data, and qualitative data from industry reports, expert viewpoints, and case-based evidence presented from prominent financial institutions. The results show that large commercial banks are more likely to have a high degree of AI integration because of their high level of technological infrastructure, data science expertise, and robust regulatory compliance. Smaller and regional banks on the other hand are facing issues like access to data privacy, cyber security, limited technical expertise, and high implementation cost. Despite such obstacles, AI has significantly transformed the way businesses are run, from streamlining processes to increasing fraud detection, and making financial services more personalized for clients. AI-powered tools like chatbots and virtual assistants are preferred by customers, as seen by the shift towards such technology in customer experience analysis. This comes from customer experience analysis, where they are increasingly likely to opt for AI-powered tools like chatbots and virtual assistants that offer 24/7 support and quicker query resolution. Furthermore, AI-driven credit assessment models have enhanced loan approval accuracy and minimized default risks. But there are some concerns about algorithmic bias, transparency in decision making and reliance on automation. The paper also explores recommendations at a higher level of strategy and policy, such as ensuring the development of ethical frameworks for AI, the implementation of more robust measures for data protection, and investment in digital banking infrastructure. The study underscores the transformative role AI plays in improving the efficiency and service in banking, but also emphasizes the need for a balance between innovation and fairness, accountability, and regulation to ensure its sustainable success in the long term. The results offer valuable evidence-based insights for the future of sustainable integration of AI in banking systems, enriching the broader body of financial technology literature.
Keywords: Artificial Intelligence, Banking Systems, Financial Technology, Fraud Detection, Credit Scoring, Chatbots, Risk Management, Algorithmic Trading, Digital Banking, Financial Automation