The Study of Machine Learning and Artificial Intelligence in the Banking Industry in Enhancing Customer Experience and Risk Management

Over the past few years, the banking sector has undergone a rapid digital transformation. The combination of AI and ML is disrupting the old age systems and rule-based systems like anything. Various sectors are benefiting from implementing Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate, personalise, and predict further changes in the customary banking model. In this paper, the study analyses the role of Machine Learning in improving customer experience and managing risk in the banking industry. Additionally, this paper discusses recent banking use-cases for chatbots, credit scoring systems, fraud detection systems, and anti-money laundering systems built using AI and ML. It also covers the ethical and regulatory aspects of AI and ML in the banking industry, including the concerns of privacy, algorithmic accountability, and algorithmic transparency. The report ends with a brief description of new technologies such as Explainable AI, Quantum Computing, and the use of Blockchain technology in financial systems. With the help of Artificial Intelligence and ML, customer experience has continued to improve, and risk management in the financial system is getting better. This study adopts a conceptual and literature-based analytical approach, and also makes an attempt to draw insights from recent empirical studies and industry reports are used to synthesise the applications of AI and ML in modern banking. The paper contributes by integrating dual perspectives, viz., customer experience and risk management, into a unified analytical framework. This study provides a structured understanding of how AI and ML combinedly enhance banking operations, offering theoretical and managerial insights for future empirical research.