secure transactions

This paper will take a critical view of issues of investment management, secure transactions, and risk in the digital era. The Financial Services Industry is a product of the Information Technology (IT) revolution. The application of IT have had profound implications for how products are produced, how they’re exchanged, who develops the new products, and how the old products are replaced. The current wave of technological evolution is changing the way in which financial services are produced and delivered. Developments in the World Wide Web sector have accounted for an expansion and strengthening of the online transaction sector, with a growing number of fully automated banking platforms. This transition has been accompanied by considerable public expenditures on new security technologies to protect it. Of particular interest in this context is the provision of data privacy and the prevention of fraud through secure transactions in order to bolster consumer and institutional confidence in the transacting financial markets. This paper will consider investment management, secure transactions, and risk assessment for online activities in the financial sector. Technological advancements in the field of IT have allowed the financial industry to take advantage of networked computing, to automate trading decisions, and to trade very quickly and in large volumes. The financial industry can now use sophisticated high performance computer driven trading platforms. These developments have had a significant impact on the way banks, fund managers, and individuals trade currency and equity. To this day most financial orders are negotiated through people, often using voice linked dealing systems. This experience has decreased considerably due to the rapid growth of automated computer systems capable of almost instantaneous order placement with the corresponding financial institutions. With appropriate technology and clear development the banks and exchanges alike can now record at high frequency virtually all transactions that occur in the financial market. Furthermore, several data vendors are now selling high frequency data in relatively cheap and easy to work formats, essentially lowering the barriers to entry in this field. Some people consider these characteristics to be sufficient to classify financial data (specifically price data) as “Big Data”.

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.