Abstract: A Monte Carlo simulation is disclosed to propagate model estimate and macroeconomic uncertainties through the calculation of lifetime loss or yield and their uncertainties using a classical computer or a quantum for the purposes of adjusting loan pricing for the uncertainty in the yield.

Abstract: A Monte Carlo simulation is disclosed to propagate model estimate and macroeconomic uncertainties through the calculation of lifetime loss or yield and their uncertainties using a classical computer or a quantum for the purposes of adjusting loan pricing for the uncertainty in the yield.

Abstract: A type of machine learning called supervised learning is disclosed. In supervised learning, the training data contains observed values of a target variable and a set of candidate explanatory variables. Supervised learning has been used previously for predicting time series such as economic time series and separately for intrinsic behavior patterns such as credit scoring for offering consumer loans. However, when creating a single model of a target variable that combines both internal behavior and external drivers, the internal behavior and external drivers are often correlated to each other as well as to the target variable. In such a system, the external drivers are usually intended to capture the time series behavior and the internal behavioral variables capture the idiosyncratic effects, but when multicollinearity occurs across all these factors, the internal behavioral variables must also be predicted before a forecast can be created for the target variable.

Abstract: A computer-implemented method and invention for calculating a loan-level model with the age, period, and cohort functions found in the structure of an Age-Period-Cohort models. The invention uses one observation per account per time period, processed with a uniquely structured set of basis functions, so that the model may be estimated with either Generalized Linear Models (GLM) or Generalized Linear Mixed Models (GLMM). The model created by the invention may be used for account-level forecasting or stress testing of the defined performance variable if the historic extrapolation of the period function is detrended, the age and cohort functions are re-estimated appropriately, and a suitable scenario for the future of the period function. Scores may also be created by combining traditional scoring inputs with an account-level offset computed as the sum of the age and period functions at each time point.

Abstract: A computer-implemented method and invention for calculating a loan-level model with the age, period, and cohort functions found in the structure of an Age-Period-Cohort models. The invention uses one observation per account per time period, processed with a uniquely structured set of basis functions, so that the model may be estimated with either Generalized Linear Models (GLM) or Generalized Linear Mixed Models (GLMM). The model created by the invention may be used for account-level forecasting or stress testing of the defined performance variable if the historic extrapolation of the period function is detrended, the age and cohort functions are re-estimated appropriately, and a suitable scenario for the future of the period function. Scores may also be created by combining traditional scoring inputs with an account-level offset computed as the sum of the age and period functions at each time point.

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.

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: 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, utlimately, for the generation of a number of forecasts for key portfolio drivers.