A Study on Impact of Generative AI on Investment Decision-making at Motilal Oswal Financial Services Ltd.

Abstract

This study, titled "A Study On Impact Of Generative AI On Investment Decisionmaking At Motilal Oswal Financial Services Ltd.," evaluates the operational effectiveness and financial feasibility of deploying Large Language Models (LLMs) and Generative AI (GenAI) in wealth management and equity research. In modern financial markets, the sheer volume of unstructured data—including corporate earnings transcripts, regulatory filings, financial news, and global market announcements—creates a cognitive bottleneck for research analysts. Traditional numerical predictive algorithms are incapable of synthesizing qualitative text files. This research explores how customized enterprise GenAI architectures automate research report writing, client query resolution, and sentiment summarization. A 5-year capital budgeting analysis (2021-2025) of a bank-led sensor deployment program is conducted using capital budgeting metrics, including Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). The data indicates that the deployment of GenAI solutions improved analyst productivity by saving an average of 12.5 hours per week on report writing, while GenAI-optimized portfolios consistently generated excess returns (alpha) over the Nifty 50 Index. The financial evaluation yields a positive NPV of 392.4 Crores and an IRR of 45.1%, far exceeding the cost of capital. The study concludes that integrating Generative AI into investment workflows is highly financially feasible and operationally sustainable, providing a significant competitive advantage for modern asset management institutions. Keywords: Generative AI, Large Language Models (LLMs), Investment Decision-Making, Motilal Oswal, Analyst Productivity, Portfolio Performance, Cost-Benefit Analysis.