Building an end to end fake news detection system consists of detecting claims in text and later verifying them for their authenticity. Although most of the recent works have focused on political claims, fake news can also be propagated in the form of religious intolerance, conspiracy theories etc. Since there is a lack of training data specific to all these scenarios, we compiled a homogeneous and balanced dataset by combining some of the currently available data. Moreover, it is shown in the paper that how recent advancements in transfer learning can be leveraged to detect claims, in general. The obtained result shows that the recently developed transformers can transfer the tendency of research from claim detection to the problem of check worthiness of claims in domains of interest.