A Managing Compliance in Financial Institution Security is required to ensure that sensitive financial transactions are carried out without incurring losses. Losses would be due to a number of factors whether internal or external, deliberate or accidental, and strongly dependent on correct and timely reactions in response to incidents. Events related to the assessment of online compliance can be classified in terms of the impact on the financial transactions e.g. fraud. Events exposing the transaction to fraud are used to generate rules to monitor cryptographic techniques applied to sensitive financial data, either as part of the transaction or for value recovery. Intelligent block-based fuzzy classification is used to determine different safety levels for different parts of the financial data thereby enabling secure trade with a lowest level of encryption and s igning overhead. This is facilitated by intelligent targeting of fraud events cutting through a range of signatures. Experiments with sets of fraud profiles derived from analysis of previous incidents employing branded-transaction card fraud are presented. In these experiments, monitoring rules are generated automatically using unsupervised neural gas clustering from detection blocks that are input to the intelligent classification engine. It is suggested that the versatility of G- Cluster in this area is demonstrated by the ability to adjust the fraud profile easily.