Accelerating Financial NLP Research at the Technical University of Munich
The Challenge At one of Europe's top technical universities, researchers and doctoral candidates faced a bottleneck in quantitative finance and NLP research. While they had access to traditional numerical databases (like Bloomberg), accessing high-volume, unstructured textual data—such as Annual Reports and ESG disclosures—was largely manual. Students spent months building scrapers and PDF parsers rather than analyzing market trends, limiting the scope of Master's theses and doctoral dissertations.
The Solution TUM partnered with FinancialReports to deploy a campus-wide data infrastructure.
- Frictionless Access: We implemented a seamless authentication layer, allowing any student with a valid university email (
@tum.de) to instantly access our web platform and API without individual procurement cycles. - AI-Ready Datasets: Instead of raw PDFs, researchers utilized our pre-parsed Markdown endpoints. This allowed the Department of Informatics and the School of Management to feed clean, structured text directly into Large Language Models (LLMs) for sentiment analysis and topic modeling.
- Bulk Historical Data: We provided deep historical archives, enabling longitudinal studies on European corporate reporting standards over the last decade.
The Result FinancialReports is now a foundational resource for the university's financial data science curriculum. By removing the "data cleaning tax," students have accelerated their research timelines by an estimated 40%, producing higher-quality papers on topics ranging from "Greenwashing Detection in ESG Filings" to "Predictive Modeling of Insider Trades."
"Before this partnership, 80% of a PhD candidate's time was spent scraping and cleaning PDFs. Now, we stream clean, structured text directly into our models. It has fundamentally expanded the scale of research we can tackle in a single semester."
