Abstract: A method and system for identifying unknown illicit networks uses node data, link data, and network data in a recursive analysis that computes node probabilities by combining patterns based on nodes, links and the topology of the network structure present simultaneously in the data. An iterative balancing algorithm is used to make the probability values self-consistent.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Abstract: A method of building predictive statistical models provides a dedicated aggregation module for each transactional record source. Each aggregation module aggregates the transactional records using a neural network function to produce a scalar output which can then be input to a traditional modeling function, which may employ either logistic regression, neural network, or radial basis function techniques. The output of the aggregation modules can be saved, and updated aggregation values can be updated by processing new transaction records and combining the new transaction values with the previous output values using a blending function. Parameters of the neural network in the aggregation module may be calculated simultaneously with the parameters of the traditional modeling module.

Abstract: The present invention maximizes modeling results for targeted marketing within a specific working interval so that lift within the working interval is higher than that obtained using traditional modeling methods. It accomplishes this by explicitly solving for lift through sorting a target list by predicted output variable outcome, calculating the integral criterion of lift for a desired range by using known response and non-response data for the target list, iterating on a set of input parameters until overfitting occurs, and testing results against a validation set.

Abstract: A method and apparatus is disclosed for determining the prepayment propensity of individual borrowers. Early payment of debt instruments, such as loans and leases, can lead to losses being suffered by lenders. The present invention analyzes the demographics associated with a particular borrower to determine both the individual and group based prepayment propensity. The history of the borrower, the history of the borrower's demographic group, interest rate trends and other factors are then used to calculate a prepayment score that can be used by the lender to determine the propensity of a given borrower to prepay the instrument in question. The score of the individual borrower can be used to estimate the profitability of a debt instrument and allow the lender to make appropriate adjustments prior to issuing the instrument. The individual prepayment scores of a lender's or broker's clients can also be used to rate the lender or broker.

Abstract: The present invention is drawn to method of building predictive statistical models based on transactional data utilizing a set of aggregation modules to provide scalar input for logistic regression, neural networks or radial basis functions models. Each transactional source of data is processed by a dedicated aggregation module. The output of aggregation modules can be saved so when the new transactional records become available the output of the model can be updated just by processing these new records only. Parameters of the aggregation module are calculated simultaneously with the parameters of the traditional module during model training.

Abstract: A method and apparatus for determining the prepayment propensity of borrowers. Earlier payment of loans and particularly mortgage loans can lead to losses being suffered by lenders. The present invention analyzes the demographics associated with a particular borrower to determine both the individual and group based prepayment propensity. The history of the borrower, the history of the demographic group to which the borrower belongs, interest rate trends and other factors are then used to calculate a prepayment score that can be used by the lender to determine the propensity of a given borrower to prepay the loan in question. Where prepayment is a significant risk, inducements to the borrower to leave the loan in force can be made or the loan product can be adjusted to reflect the prepayment risk involved. Loan brokers can also be rated based upon the prepayment propensity of those borrowers who are clients of the broker.