Financial Insight Engine

Project Overview

For a quantitative hedge fund, I developed a secure, in-house Large Language Model designed to analyze and summarize thousands of pages of financial reports, earnings calls, and market news.

The Challenge

Analysts were spending a significant portion of their day manually reading dense financial documents to extract critical insights. This process was slow, prone to human error, and made it difficult to react quickly to market changes.

My Solution

The solution was an end-to-end “Insight Engine” that could:

Ingest Data Securely: Connect to internal and external data sources via secure APIs.

Process and Summarize: Use a custom LLM, fine-tuned on financial terminology, to extract key metrics, identify trends, and generate concise summaries.

Utilize RAG: Implement a Retrieval-Augmented Generation (RAG) system to ensure all generated insights were directly traceable to the source documents, preventing hallucinations.

This tool is now a core part of the firm’s daily workflow, allowing analysts to cover more ground and make faster, more informed decisions.