Abstract:
Presented are a system, method, and apparatus for loan risk assessment by assignment of a specific loan account to a loan cluster of a plurality of loan clusters. A computing device receives plurality of loan account histories describing a plurality of loan accounts during a training phase. An appropriate supervised classification method is applied to the loan account histories to obtain a mathematical description of loan cluster set. Next, the computing device receives a test loan account payment history describing a test loan account to be analyzed. The test loan account is assigned to at least one cluster of the previously trained cluster set. One or a plurality of causes is then determined for assigning the test loan account to the cluster set; and a predicted risk value for the test loan account is determined based on the cluster the test loan account is assigned to.
Abstract:
A system, method, and apparatus for determining risk associated with a plurality of loan accounts, having an off-line mode and an online mode. In the off-line mode a first plurality of account histories is received. A maximum value variable m is set. A definition is received of a predetermined maximum look-ahead timeframe p. An iterative variable i is set equal to zero. While i is less than the maximum value variable m, a plurality of variables associated with an account history equaling the iterative variable i are stored and i incremented by 1. A predictive multi-output risk model is trained. In the online mode, a second plurality of account histories is received. A determination is made which accounts have a future risk level greater than a current risk level, and a further determination made which accounts currently require one or more tasks. Accounts requiring tasks are automatically assigned.
Abstract:
Presented are a method, system, and apparatus for semi-automatic and automatic loan risk targeting and action prioritization in loan monitoring applications. In an off-line mode, a computing device associated with a multi-window computer-based tool receives a plurality of loan account histories for loan risk analysis. A predictive multi-output risk model is trained with the received plurality of loan account histories, the predictive multi-output risk model indicating a risk level associated with each of the loan accounts. In an online mode, the user is presented an option for semi-automatic loan analysis, in which the user is presented with output of a predictive multi-output risk model associated with the plurality of loan accounts. The user is also presented with the option for automatic loan analysis, allowing the user to be automatically presented with loan accounts at a greatest level of risk of all loan accounts.
Abstract:
The disclosed embodiments illustrate methods and systems for creating a simulator for a crowdsourcing platform. The method includes generating a plurality of rules indicative of at least one of a behavior or an interaction, of one or more entities associated with the crowdsourcing platform, based on one or more parameters associated with each of the one or more entities. Thereafter, a first level of service of the crowdsourcing platform is estimated based on the generated plurality of rules. Further, the plurality of rules are modified based on the first level of service and an observed level of service of the crowdsourcing platform. The plurality of rules are modified such that a second level of service of the crowdsourcing platform, estimated based on the modified plurality of rules, approaches the observed level of service of the crowdsourcing platform. The modified plurality of rules corresponds to the simulator for the crowdsourcing platform.
Abstract:
A transportation service data assessment system includes a data set holding f performance metrics for various route components of a transportation service. When the system receives a selected set of operational data parameter labels, as well one or more route components, it develops a matrix of performance metrics corresponding to the operational data and route components, determines a distance between each row of the performance metric matrix to yield a multi-dimensional matrix, and maps the distance data to a 2-D or 3-D coordinate set so that to yield a coordinate matrix. The system groups the data of the third matrix into clusters and presents the clusters on a display so that outliers are visually distinguished from clustered items, and so that redundant items are also visually apparent in the clusters.
Abstract:
A method for processing black point compensation parameters for a color image to be printed so as to enhance image quality of the color image is provided. The method includes converting a received color image to a gray scale image; determining, using the received color image and the gray scale image, a performance attribute to estimate the effect of the black point compensation parameters on the image quality of the received color image, respectively; deriving a model to estimate relationships between the black point compensation parameters and the determined performance attribute; maximizing the performance attribute of the derived model so as to process the black point compensation parameters for the color image; and using the processed black point compensation parameters to construct output device profiles.
Abstract:
A method for processing black point compensation parameters for a color image to be printed so as to enhance image quality of the color image is provided. The method includes converting a received color image to a gray scale image; determining, using the received color image and the gray scale image, a performance attribute to estimate the effect of the black point compensation parameters on the image quality of the received color image, respectively; deriving a model to estimate relationships between the black point compensation parameters and the determined performance attribute; maximizing the performance attribute of the derived model so as to process the black point compensation parameters for the color image; and using the processed black point compensation parameters to construct output device profiles.
Abstract:
Presented are a system, method, and apparatus for loan risk prediction. A computing device receives a plurality of loan account histories containing variables x; a plurality of algorithms then independently selects features from the loan account histories, the selected features being functions of the received variables x; the selected features are then grouped into a first data structure xf; the computing device applies voting algorithm(s) to the selected features to create a second data structure xr; the computing device generates a third data structure xI of interaction terms from the second data structure xr; a fourth data structure is generated, xNL, where xNL=xr∪xI or x∪xI; a model executes that selects significant features from the fourth data structure xNL; and a nonlinear model y=f(XNLR) is generated, the nonlinear model y indicating risk associated with the plurality of loan account histories.
Abstract:
A print job processing system determines a set of job size thresholds for a set of print jobs received over a period of time by a print shop. The print shop includes multiple cells. The system orders the job size thresholds from lowest to highest, assigns the lowest job size threshold to a first one of the cells, assigns the highest job size threshold to a second one of the cells, and assigns each of the remaining thresholds to the remaining cells so that each of the cells has an assigned threshold. Then, when the system receives a print job, it determines a size for the received print job, identifies which of the cells has an assigned job size threshold that corresponds to the size of the received print job, and routes the received print job to the identified cell. The identified cell may then process the received print job.
Abstract:
A system for analyzing transportation service performance receives a first parameter of interest for the service and identifies an historic trending change in the parameter of interest. The system then accesses a data set of a set of additional performance parameters to determine which of the additional parameters most influenced the change in the first parameter. The system may do this by identifying trending changes for each of the additional parameters and automatically determining which of the additional parameters influenced the change in the parameter of interest.