Abstract
Understanding, analyzing, and constructing experimental models of real life financial systems is a wide-ranging task that requires an integration of different technologies and enabling tools and calls for a bridging of the gap between theory and application as well as research and development in this area. Business process modelling, intelligent state and agent-oriented modelling, data warehousing and data quality assessment tools, financial analysis tools, and client server technologies should tie up coherently to enhance knowledge acquisition in a global financial market. Players in the global financial marketplace, such as investors, rating agencies, financial service providers, analysts and consultants, are faced with a massive amount of explicit and implicit market information characterized by a high level of dependency and interrelationship. An automated environment which, as far as possible, depicts and captures all aspects of the real-life financial system, is needed to adapt to state changes in global financial markets. Such an environment should provide a flexible human computer interface backed with a high level of interaction, visualization and reporting capability, with minimal overhead coding requirements.