A fraudulent financial statement involves the intentional furnishing and/or publishing of false information in it and this has become a severe economic and social problem. We consider Data Mining (DM) based financial fraud detection techniques (such as regression, decision tree, neural networks and Bayesian networks) that help identify fraud. The effectiveness of these DM methods (and their limitations) is examined, especially when new schemes of financial statement fraud adapt to the detection techniques. We then explore a self-adaptive framework (based on a response surface model) with domain knowledge to detect financial statement fraud. We conclude by suggesting that, in an era with evolutionary financial frauds, computer assisted automated fraud detection mechanisms will be more effective and efficient with specialized domain knowledge.