The automatic detection of uncertain statements can benefit NLP tasks such as deception detection and information extraction. Furthermore, it can enable new analyses in social sciences such as business where the quantification of uncertainty or risk plays a significant role. We approached the automatic detection of uncertain statements as a binary sentence classification task on the transcripts of spoken language for the first time in the financial domain. We created a new dataset and - besides using bag-of-words, part-of-speech tags, and dictionaries - developed rule-based features tailored to our task. Finally, we analyzed systematically, which features perform best in the financial domain as opposed to the previously researched encyclopedic domain.