Credit reports about German companies
Hello, In addition to my studies in computer science, I have been working on a side project. I obtain data from the Unternehmensregister, a register where every German limited company is required to publish their financial statements. These statements are published as HTML files and are completely unstructured. While financial statements often look similar, companies are not required to follow a specific structure, which often leads to inconsistently formatted statements. The use of the Unternehmensregister is completely free, so you can check out some examples. I wrote code that converts…
In plain words
This tool extracts and structures financial data from German company filings published in the Unternehmensregister. It converts unstructured HTML financial statements into organized data using the ChatGPT API, then applies a Random Forest algorithm to estimate default probability. The product is designed for developers and financial analysts who need clean, analyzable data from German limited companies. The source data is freely available, making this useful for credit risk assessment and research.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello, In addition to my studies in computer science, I have been working on a side project. I obtain data from the Unternehmensregister, a register where every German limited company is required to publish their financial statements. These statements are published as HTML files and are completely unstructured. While financial statements often look similar, companies are not required to follow a specific structure, which often leads to inconsistently formatted statements. The use of the Unternehmensregister is completely free, so you can check out some examples. I wrote code that converts the unstructured financial statements into structured data using the ChatGPT API. This works well. Of course, there are some problems that have not yet been solved, but data extraction works well for the majority of companies. I than coded a Random Forest algorithm to estimate the probability of default for a company based on its financial statement from the Unternehmensregister. I built a website to present the structured data along with the scores. Essentially, I create a credit reports for companies. Currently, there are four companies in Germany that also create credit reports (Schufa, Creditreform, Crif, and Creditsafe). Other companies resell the data from these four providers. I provide the same services as these companies, but without including personal information such as directors or investors. The market for this service is quite large; for example, Creditreform sold over 26 million credit reports about companies in 2020. My probability of default prediction performs quite well, achieving an AUC score of 0.87 on my test data. An AUC of 0.87 means that there is an 87% chance that the model ranks a randomly selected company that defaults higher than a randomly selected company that does not default. Additionally, there are many more companies to crawl for my database. Currently, I am focusing on companies that are required to publish their profit and loss statements. For testing purposes, there are currently 2,000 companies available on my website. At the moment, the website is only available in German, but you can use Google Translate, which works ok for my website. Thank you very much for your feedback!
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