Annual financial reports play an important role in the financial audit process. Auditors usually check the numbers in financial statements. The text of the reports could also provide valuable information but it is very time-consuming to check. In addition, the introduction of XBRL (eXtensible Business Reporting Language) as a requirement for tagging reported financial values, introduces more challenges for auditors. We aim to automate the analysis and tagging of the text of financial reports using Machine Learning. To this end we focus on the following research challenges: (a) Classification of long texts with imbalanced class distribution (b) Numeric Entity Recognition.