Abstract: A method, system, and computer program for solving the inverse problem through an iterative process whereby each iterative effectively solves one forward problem without having to sample the entire non-linear space. The method is a selective and iterative process for optimizing many variables that substantially achieves a global optimum solution. One particular process utilizes a neo-Darwinism method. Under this method, the sample space is iteratively analyzed via “mutations” to the value of the variable involved. Starting from a basic structure that is assumed sub-optimal, small variations or mutations are applied to each variable in turn, and those that are determined to improve the outcome value are kept. A better outcome value is determined to exist when a set of ratings is closer to the required set. Because the average rating is an invariant, the variable space is operated on throughout the process of looking for the combination of factors that will lead to the better outcome value.

Abstract: A method, system, and control program for expediting payment of claims to service providers and to reduce institutional capital reserves. The method includes evaluating a risk of full payment of each claim; grouping claims from a service provider(s) based on a commonality of risk; generating a security representative of the risk of the grouped claims and an investment value of the security; and exchanging a pre-payment amount to medical service providers for the security. More specifically, evaluating the risk of full payment includes comparing each individual claim to a database of historical performance of the service provider and of similar claims and, moreover, evaluating for each claim an expected payment amount and an expected time (or delay) of payment by an obligor.

Abstract: A method for assessing and dynamically rating transactions (180) for structured finance transactions. The method assesses the deviation (170) from a payment promise to be expected from securities backed by pools of assets of various forms (100), the securities being issued in a plurality of tranches (125). The liabilities of the transaction, including triggers and external form of credit enhancement, are taken into account precisely to compute the deviation from the payment promise to be expected by liability holders. Data representing the structure of the transaction and the current state of the asset pool are received (100). A Markov chain formalism (150) is applied on the received data, and a cash flow model is constructed to predict the cash flow performance (180) of the asset pool.

Abstract: A method for assessing and dynamically rating transactions (180) for structured finance transactions. The method assesses the deviation (170) from a payment promise to be expected from securities backed by pools of assets of various forms (100), the securities being issued in a plurality of tranches (125). The liabilities of the transaction, including triggers and external form of credit enhancement, are taken into account precisely to compute the deviation from the payment promise to be expected by liability holders. Data representing the structure of the transaction and the current state of the asset pool are received (100). A Markov chain formalism (150) is applied on the received data, and a cash flow model is constructed to predict the cash flow performance (180) of the asset pool.