This paper discusses a subclass of fraud detection systems known as break detection systems. A number of AI techniques have proven effective in fraud detection; however break detection systems address more complex problems than have typically been reported. Problems addressed by break detection systems are characterized not only by dynamic data with complicated temporal and other relationships, but also by changing types of fraudulent behavior. Furthermore, the available data usually does not provide the appropriate representation for direct search. In particular, we note the essential role of consolidation and linkage in these systems to build abstractions that can support break detection. The authors have had experience designing and building two break detection systems in related domains of financial transactions - one to discover occurrences of money laundering involving large cash transactions reported to the U.S. Treasury, the other to discover potentially violative behavior of stock broker/dealers in the Nasdaq Stock Market. We discuss problem domain characteristics, AI techniques, and lessons learned about break detection in the context of the architectures of these systems.