This paper presents initial experiments in data-driven morphological analysis for Finnish using deep learning methods. Our system uses a character based bidirectional LSTM and pretrained word embeddings to predict a set of morphological analyses for an input word form. We present experiments on morphological analysis for Finnish. We learn to mimic the output of the OMorFi analyzer on the Finnish portion of the Universal Dependency treebank collection. The results of the experiments are encouraging and show that the current approach has potential to serve as an extension to existing rule-based analyzers.