Finnecs: An AI-Powered Platform for Financial Data Analysis and Insight Generation

Abstract

The exponential growth of financial information presents significant challenges for individual investors attempting to make informed decisions. Traditional approaches to financial analysis remain limited by human processing capacity and the complexity of modern markets. This engineering thesis presents a comprehensive platform that addresses information overload in financial markets through automated data processing and artificial intelligence. The system automatically monitors multiple data sources and generates actionable insights by analyzing unstructured financial documents using advanced language processing techniques. The platform is architected as a modular system with containerized components, featuring distributed data pipelines and machine learning workflows. A key contribution of this work is the validation of self-hosted infrastructure as a cost-effective alternative to traditional cloud-based solutions. The outcome demonstrates a functional system that transforms complex financial data into structured insights for individual investors, achieving order-of-magnitude cost reductions compared to traditional cloud infrastructure. This work presents a practical architecture for improving access to sophisticated financial analysis through modern computational approaches