Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy.

The treatment of vulnerability at the 1988 Risk Model Builders' Workshop is examined, and a definition of vulnerability that is intuitively satisfying and provides a foundation upon which mathematical models can be built is developed. Two vulnerability models that together appear to capture the general conceptualizations of vulnerability espoused by other authors are presented. The authors also discuss the ongoing development of their expert system for risk management (M/sup 2/RISK), which will utilize knowledge about vulnerabilities of information systems and their components. M/sup 2/RISK is designed to eventually function as a full risk-management system with interface tools that will allow rapid specification of systems and easy management of system changes, and generally aid the risk-management process.>