Developed an AI-powered financial advisor using LLMs and real-time market data, enabling personalized investment recommendations and improving user engagement by 32%. Created a Django REST API integrating JWT auth, portfolio generation, and chatbot with LLMs, supporting over 100 user interactions. Designed and implemented a real-time sentiment analysis module using Python and OpenAI APIs, enhancing the AI financial advisor’s responsiveness. Automated ETL pipelines for price and news data using Python and cron jobs, reducing manual intervention by 95%. Built forecasting and pricing simulations to align investment allocations with expected returns and risk levels.

Led loan portfolio sales totaling USD 40M, increasing profitability by 25%. Built financial forecasting models using machine learning, reducing overdrafts by 10%. Streamlined expenditure tracking, reducing discrepancies by 20%. Enhanced financial data retrieval by 30% via optimized SQL queries. Integrated business logic into SQL workflows, enabling dynamic financial reports used by C-level decision makers. Improved revenue projection accuracy by 20% through automated financial models. Identified patterns in client behavior that led to a 12% increase in customer lifetime value.