Abstract: The present invention relates to a method that allows managers of retail portfolios to compute performance time series that have been cleaned of marketing impacts, lifecycles, management actions, and seasonality, leaving only the performance changes due to the environment. These normalized series can be used to compute the necessary covariance matrices for portfolio optimization or computing portfolio-level economic capital. The invention applies to any retail product or segment where vintage-level performance time series are being stored.

Abstract: A method predicts vintage behavior. The method receives a first set of data, the first set of data being based on at least one of a first factor and a second factor. The first factor includes a factor related to vintage maturation. The second factor includes a factor not related to vintage maturation. The method determines a second set of data and a third set of data based on the first set of data, the second set of data being based on the first factor, and the third set of data being based on the second factor. The method then predicts vintage behavior based on at least one of the second set of data and the third set of data.

Abstract: A system and method for segmenting and clustering consumer segments is disclosed. The system and method disclosed decompose micro-segments into maturation curves, exogenous curves and scaling parameters. The system and method of the present invention use these generated curves to cluster micro-segments into macro-segments for business analysis purposes.

Abstract: Generation of risk-related retail lending portfolio scenarios is disclosed. A selected functional form is used to decompose vintage performance data into a maturation component, an exogenous component and vintage calibration parameters for the portfolio. Known exogenous drivers are extracted from the exogenous component to create a residual exogenous component. Monthly changes in the residual exogenous component are computed, and the distribution of monthly changes in the residual exogenous component is measured. This information is used to generate a number of random potential future scenarios for the residual exogenous component and, ultimately, for the generation of a number of forecasts for key portfolio drivers.