Impact of Artificial Intelligence on Financial Decission Making in Bank Institute

Abstract

This study, titled "Impact of Artificial Intelligence on Financial Decission Making in Bank Institute," evaluates the integration of predictive machine learning models, robotic process automation (RPA), and RegTech compliance dashboards in commercial banking decisionmaking. Artificial Intelligence (AI) has redefined credit risk appraisals by processing borrower transactional data in real-time, reducing default risks and processing costs. A fiveyear project lifecycle (2021-2025) of a retail banking clusters AI underwriting deployment is analyzed using standard capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative metrics demonstrate that credit risk underwriting constitutes 38% of AI budgets. Implementing AI underwriters reduces credit evaluation latency to 3 minutes, while decreasing annual credit default rates to 1.1% and increasing automated approval rates to 78% by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, indicating strong project feasibility. The study concludes that AI integration is highly viable, offering commercial banks substantial operational efficiency, enhanced asset quality, and robust capital adequacy. Keywords: Artificial Intelligence, Financial Decision Making, Credit Underwriting, Credit Defaults, Automated Approvals, Capital Budgeting, Financial Feasibility, Bank Institute.