Abstract: A computer system for modeling a portfolio of products in a financial system to determine the rate of a target product. The products are defined by attribute values, an attribute being any criteria that impacts product rates. Linear associated product rules are used by the computer system to create an optimized scenario of total profit and overall volume of sales for the portfolio. From the optimized scenario a rate for the target product can be determined which maintains a financial institution's strategic and business objectives. The optimizing process includes applying the associated product rules to products actively contributing to key performance indicators. Densification is then used to infer the rate for all other products in the portfolio. Finally, if the starting rate of a product violates an associated product rule, the starting rate is relaxed to avoid the violation.

Abstract: A computer implemented method for determining the reference values of sensitivities and strategies for price optimization demand models from a profit function and current product price. A total profit objective is expressed as the maximization of profit and volume, where a strategy parameter represents the relationship between profit and volume. From the total profit objective, the bounds of the strategy parameter are expressed as conditional inequalities relating the bounds to functions of the unit profit at the current rate and average volume. The strategy parameter is then set to the average of these bounds. The reference elasticity is expressed as a function of the unit profit function and average volume. The resulting reference values can be used in a price optimization system to generate recommended prices that relate to an industry's current pricing scheme.

Abstract: A computing system (100) receives transaction records (130) for loans taken at various interest rates (1904) for a loan segment (902). Performance indicators (1716) indicative of customer behaviors (1702) are computed (1806) using independent demand models (300, 302, 304, 306, and 308). Computing system (100) includes a performance indicator forecaster (112) that determines relationships between the performance indicators (1716) and various prices, or interest rates (1904). These relationships can include profit (1906) and/or volume (1908) relative to the various interest rates (1904). The relationships are utilized to select an interest rate (1912, 2102) for the product segment (902) for implementation by a financial institution.

Abstract: A non-stationary time series model using a likelihood function as a function of input data, base demand parameters, and time dependent parameter. The likelihood function may represent any statistical distribution. The likelihood function uses a prior probability distribution to provide information external to the input data and is used to control the model. In one embodiment the prior is a function of adjacent time periods of the demand profile. The base demand parameters and time dependent parameter are solved using a multi-diagonal band matrix. The solution of base demand parameters and time dependent parameter involves making estimates thereof in an iterative manner until the base demand parameters and time dependent parameter each converge. A non-stationary time series model is provided from an expression using the solution of the base demand parameters and time dependent parameter. The non-stationary time series model provides a demand forecast as a function of time.