In this paper we describe the results achieved using the JAM distributed data mining system for the real world problem of fraud detection in financial information systems. For this domain we provide clear evidence that state-of-the-art commercial fraud detection systems can be substantially improved in stopping losses due to fraud by combining multiple models of fraudulent transaction shared among banks. We demonstrate that the traditional statistical metrics used to train and evaluate the performance of learning systems, (i.e. statistical accuracy or ROC analysis) are misleading and perhaps inappropriate for this application. Cost-based metrics are more relevant in certain domains, and defining such metrics poses significant and interesting research questions both in evaluating systems and alternative models, and in formalizing the problems to which one may wish to apply data mining technologies.This paper also demonstrates how the techniques developed for fraud detection can be generalized and applied to the important area of Intrusion Detection in networked information systems. We report the outcome of recent evaluations of our system applied to tcpdump network intrusion data specifically with respect to statistical accuracy. This work involved building additional components of JAM that we have come to call, MADAM ID (Mining Audit Data for Automated Models for Intrusion Detection). However, taking the next step to define cost-based models for intrusion detection poses interesting new research questions. We describe our initial ideas about how to evaluate intrusion detection systems using cost models learned during our work on fraud detection.