Artificial Intelligence/Machine Learning Driven Decision making in Business Analytics for Financial Sector using Ensemble Machine Learning Techniques

In today's rapidly evolving financial landscape, banks stand in critical conditions to capitalize on advanced technologies to enhance their competitiveness. By harnessing AI/ML models, finance sectors like banks can optimize customer interactions, streamline operations, and drive down expenses while delivering unparalleled user experiences. These powerful tools enable banks to excel in three critical domains: real-time customer engagement, automated workflows, and data-driven decision-making. Deploying AI/ML solutions at scale offers a distinct advantage over competitors, fostering substantial gains for clients, stakeholders, and the institution alike. Credit card risk assessment, insurance claim prediction, and targeted marketing initiatives are just some examples where AI can deliver exceptional results. Algorithms like K-Nearest Neighbors, Random Forest, Support Vector Machines, and Logistic Regression, in this paper prove effective in classifying decision-making problems within the finance sector with great recall, precision and F1 Score above 0.8 in majority cases. We have well utilized stacking classifier ensemble technique for better results.. A comprehensive approach to AI integration encompasses all aspects of the business, including product development, risk management, compliance, and customer service. By embracing this transformational technology, banks can achieve sustainable growth, foster innovation, and maintain a strong foothold in the ever-changing world of finance. As AI continues to revolutionize the banking industry, forward-thinking organizations will reap the rewards of early adoption, setting new standards for excellence and efficiency. (Abstract)