There are several arguments that the current global financial meltdown is the consequence of abusive use of risky holistic risk management methods such as Monte Carlo techniques by management and professionals. Note that Monte Carlo is a very opportunistic short term solution method that does not reflect real risk. By using such method bankers’ were encouraged to risky lending and resulted in financial credit crisis. They prescribed risk models which in essence hide risk involved so it is a time to implement sound predictive risk management model based on empirical data as described in this paper by using Complex Event Processing which a an emerging technology. Event processing has helped companies to identify and react to situations quickly and effectively, and there are many solutions that monitor various events happening both within an enterprise and outside. However, there are some situations that still require manual effort and intelligence to identify and react to those situations. Financial Institutions and Credit Rating Agencies today hope to gain a competitive edge based on their ability to respond quickly to threats and opportunities. That edge can come from highly responsive, near-real-time systems based on complex event processing (CEP) techniques used for financial applications. Now these CEP techniques are being used more widely in enterprise applications. This paper introduces the methodology of complex event processing to Credit Risk Management model. This involves the in-memory retention and processing of information, allowing the user to minimize the time taken to identify a situation (Risk Category) and the initiation of subsequent actions, business credit authorization and monitoring decisions. © Research India Publications.