Nowadays, fact-check has become an important problem to address. Although there has been immense exploration and research in this direction, considering it from financial data perspective is still unexplored. This paper presents a new deep learning-based multi-modal approach for fact-checking of financial claims. MuDal-FinFaCk, a new model is considered which is responsible for classifying financial claims as either supported or refuted based on textual and image-related evidences. Our proposed model is evaluated on two benchmark datasets, and it shows impressive results in terms of F-score and Accuracy. Also, it performs significantly better as compared to the relevant methods and previous works.