  Natural language processing methods have several applications in automated
auditing, including document or passage classification, information retrieval,
and question answering. However, training such models requires a large amount
of annotated data which is scarce in industrial settings. At the same time,
techniques like zero-shot and unsupervised learning allow for application of
models pre-trained using general domain data to unseen domains.
  In this work, we study the efficiency of unsupervised text matching using
Sentence-Bert, a transformer-based model, by applying it to the semantic
similarity of financial passages. Experimental results show that this model is
robust to documents from in- and out-of-domain data.
