Recent years have witnessed increased interests in financial fraud detection and prevention. This is driven by the ever-worsening financial crisis and an increased awareness of the importance of financial risk management. Indeed, the wide availability of fine-grained financial data enables unprecedent opportunities to change the computing paradigm for financial fraud detection and prevention. However, as these financial data become more detailed and multi-dimensional, it becomes ever more difficult for analysts to sift through the data even though it may contain valuable information. Data Mining holds great promise to address this challenge by providing efficient techniques to uncover useful information hidden in the large data repositories. Along this line, in this talk, we focus on introducing the unique features that distinguish data mining techniques from traditional analytic techniques for fraud detection and prevention. Also, as a pilot feasibility study, we will present some real-world case studies to illustrate the applications of data mining techniques for financial fraud detection and prevention. Finally, an examination of major research needs in exploiting data mining techniques for fraud detection and prevention reveals some new opportunities for bio-inspired collaborative fraud detection and prevention in multi-source and multi-level financial data.