Abstract: The present invention generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present invention relates to rating consumer lenders based on the predicted spend capacity of their consumers.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present invention relates to rating consumer lenders based on the predicted spend capacity of their consumers.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present invention relates to rating consumer lenders based on the predicted spend capacity of their consumers.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Abstract: A method for irrigating terrain comprising the following steps: generating field surface relative elevation data set using Monte-Carlo simulation; adjusting the simulation data set to ensure it falls in a reasonable numerical range; adjusting the data set to establish certain spatial dependence using Kriging interpolation method; and making further statistical characteristics parameter modification to the data set, so that the final corrected field surface relative elevation data shares the same or similar statistics characteristics parameters as the initial simulation. The method factors in both the randomness and spatial dependence of the field relative elevation distribution, which brings the simulation result closer to the actual micro-topography.

Abstract: The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer's spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.