actions data in order to predict potential fraud cases. If we take neural networks as an example, usually different phases are considered. An identification phase where the detection algorithm ana- lyzes the data and informs about any suspicious behavior. At this stage, some parameters are needed and could be setup randomly. Based on the amount of the phase error calculated in each fraud identification step, the parameters are corrected. This can be achieved iteratively and according to some specific rules until the phase error is reduced to zero. In SAFER, appropriate adaptive algorithms will be investigated. Of par- ticular interest is the assessment of the correctness of the estimations in terms of false alarms and the speed at which new variations of a known fraud scenario can be detected. 24.3.3 Functional Architecture Based on the research work dedicated to analyzing detection and prevention mechanisms, the SAFER fraud detection framework will be developed. In designing this framework, we aim at achieving the following. Highly accurate fraud detection. Low complexity system. Highly scalable and resource efficient detection. Customisable and adaptable fraud detection system. Advanced visualisation for human operators. C 2009 by Taylor & Francis Group, LLC SIP Handbook: Services, Technologies, and Security Interface Configuration policies interfaces Detection algorithms Monitoring Alarms FIGURE 24.2 Functional architecture of the SAFER fraud detection framework. On the other side, this framework will provide the following components, see also Figure 24.2. The fraud detection core: This part of the system provides the core components that include the parts of the system that can be reused for different versions of the system and different usage scenarios. - System monitoring: The monitoring activities will deal in par- ticular with the following: Call start and end time, corresponding dates, average call dura- tion, number of calls to/from a different provider, number of weekdays calls (originated or received)