Challenge
At a major German bank, thousands of final reports, each with hundreds of pages, were to be examined for risks / anomalies. The goal was to optimize the portfolio and provide a better basis for investment decisions.
Solution
Using a natural language processing framework that performs feature engineering based on financial statement text, our Data Scientists were able to perform anomaly detection and supervised machine learning using historical company information such as sales in subsequent years or bankruptcies.
Added value
Highlights