Abstract: Some implementations can include a computerized method, system or computer readable media for generating a manufacturing defect risk assessment model. The method can include obtaining training data for a plurality of loans, the training data can include loan information and a forensic audit finding and deficiency code associated with each loan. The method can also include cleaning the training data to obtain data associated with a time of origination for each loan, and enriching the training data for each loan by adding additional data. The method can further include grouping deficiency codes into one or more classes of defects, in which each class includes one or more related defect codes. The method can also include selecting one or more variables for the manufacturing risk assessment model and assigning a coefficient to each selected variable.

Abstract: Some implementations can include a computerized method, system or computer readable media for multi-dimensional risk assessment. The method can include obtaining loan application data and preprocessing the loan application data including limit checking values and augmenting the loan application data with information from external sources. The method can further include selecting scoring values corresponding to key scoring variables and scoring the application by processing the scoring values in a multi-dimensional risk assessment system including an operational risk model.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: A mobile light is disclosed that produces a high level of illumination without glare using the DC power of a vehicle's battery and charging system. The mobile light uses a high-intensity discharge lamp with a control circuit that initially ignites the lamp using a voltage multiplier circuit and that maintains illumination of the lamp using an FET switching circuit that applies a lower AC voltage to the lamp. The microprocessor also periodically adjusts the power delivered to the lamp to compensate for adverse conditions, such as high temperatures within the work light, or a high or low DC input voltage. The microprocessor also periodically senses the level of illumination provided by the lamp and adjusts the voltage applied to the lamp accordingly.

Abstract: A mobile light is disclosed that produces a high level of illumination without glare using the DC power of a vehicle's battery and charging system. The mobile light uses a high-intensity discharge lamp with a control circuit that initially ignites the lamp using a voltage multiplier circuit and that maintains illumination of the lamp using an FET switching circuit that applies a lower AC voltage to the lamp. The microprocessor also periodically adjusts the power delivered to the lamp to compensate for adverse conditions, such as high temperatures within the work light, or a high or low DC input voltage. The microprocessor also periodically senses the level of illumination provided by the lamp and adjusts the voltage applied to the lamp accordingly.

Abstract: According to the present invention, a programmable electronic timing device includes two sets of counter circuits that count minutes, hours, and twelve hour intervals. Both sets of counter circuits feed time data into a time or tide data selector circuit under control of an oscillator circuit to alternately operate a display driver circuit that drives a display means to display real time and then when the next high or low tide is to occur. One of the sets of counter circuits advances the time to the next time that high or low tide is to occur, and, when the high or low tide has been reached, the next high or low tide will be displayed alternately along with the real time.According to another aspect of the present invention, colored lights indicate whether the tide that is being indicated to occur will be high or low tide.