The renewed emphasis of the financial industry on quantitatively sound risk management practices comes with formidable computational challenges. In fact, standard approaches for the calculation of risk require repeating the calculation of the P&L of the portfolio under hundreds of market scenarios. As a result, in many cases these calculations cannot be completed in a practical amount of time, even employing a vast amount of computer power, especially for risk management problems requiring computationally intensive Monte Carlo (MC) simulations. Since the total cost of the through-the-life risk management can determine whether it is profitable to execute a new trade, solving this technology problem is critical to allow a securities firm to remain competitive.