Design and Deployment of a Robo-Advisory Portfolio Recommendation Engine for Indian Ret...

Objective: To develop an intelligent robo-advisory engine that recommends personalized investment portfolios to retail investors based on Indian market conditions and user-specific risk profiles.

Problem & Context: Indian retail investors often lack access to professional financial advice due to cost and complexity, resulting in suboptimal investment decisions. The rise of fintech platforms has created demand for algorithm-driven portfolio advisory solutions tailored to local regulations, products, and risk tolerance.

Approach & Methodology: The student team will research Indian investor behavior, regulatory guidelines (SEBI, RBI), and analyze historical market data. They will design a recommendation engine using machine learning and financial theory, incorporating user profiling, asset allocation, and real-time data integration.

Deliverables & Analysis: The final deliverables include a prototype web application or dashboard, detailed technical documentation, backtesting results, and case studies demonstrating portfolio recommendations for sample investor personas. Analytical validation will be performed using Indian equities, mutual funds, and fixed income datasets.

Business Impact: The solution addresses the accessibility gap in investment advisory services, enabling fintech firms to enhance customer retention and trust. It informs product design decisions, improves investor outcomes, and aids compliance with Indian regulations.