The human immune system can provide many metaphors that can be utilized effectively in the field of machine learning. These metaphors have been successfully applied to the complex real world problem of mortgage fraud detection, using a learning system known as Jisys. The Jisys system identifies patterns in mortgage fraud data by constructing an immune network, which is then evolved through the analysis of additional fraudulent and non-fraudulent applications. By viewing this network, a human expert can gain a better understanding of the fraudulent behavior. This paper describes significant developments over the original Jisys system. For example, the network is currently a flat structure with significant groupings within it. However, it can be difficult to identify these groups, to analyze them and then determine their significance. This paper describes some advances, which significantly improve the interpretation of the network. We also consider various statistical techniques which can be used to enhance the performance of the Jisys system as well as exploiting inherent properties of the network, which enable significant performance improvements to be implemented (with not loss of information content). Finally, the paper presents some analysis of the structures generated by the Jisys system and relates them back to the known structures in the training data set.