Abstract: A short sale monitoring system, method and computer program product assist in identifying potential fraudulent activity associated with short sale real estate transactions. A processing circuit receives property identification information for a property, a short sale notification regarding a short sale of the property, and a short sale price. The processing circuit then queries a database of loan records to identify a corresponding loan application for the property. The loan application is at least one of a pending loan application and a closed loan application for the property. The processing circuit then generates a notice when (1) the short sale price is less than a predetermined amount with respect to a price included in the loan application, and (2) a date of the loan application is within a predetermined time of a closing date of the short sale.

Abstract: Embodiments include systems and methods of detecting and assessing multiple types of risks related to mortgage lending. One embodiment includes a system and method of detecting and assessing risks including fraud risks, early payment default risks, and risks related to fraudulently stated income on loan applications. One embodiment includes a computerized method that includes creating a combined risk detection model based on a plurality of risk detection models and using the combined risk detection model to evaluate loan application data and generate a combined risk score that takes into account interaction of different types of risks individually and collectively detected by the plurality of risk detection models.

Abstract: Computer-based processes are disclosed for efficiently mining and analyzing information associated with particular mortgage loans, borrowers and properties. The disclosed processes use property-related data aggregated from multiple sources and jurisdictions to, among other tasks, identify properties owned by an individual.

Abstract: A method and apparatus for valuing every property in a predetermined geographic region at regular intervals and storing those valuations for ready access later in a layered data stratum, using customary sources of property valuation data to create a new layers of the data stratum and using the data stored in the one or more layers of the data stratum for the creation of tables, spreadsheets and maps for evaluations of changes and trends in property valuation.

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: An apparatus, method and computer program storage device determine a composite hazard index. An interface receives a first risk score for a first hazard and a second risk score for a second hazard. The first risk score is in a first range of scores and the second risk score is in a second range of scores. A processing circuit emphasizes at least some scores in at least one of the first range of scores and the second range of scores. The processing circuit also normalizes the first risk score with respect to the first range of scores and second range of scores, and normalizes the second risk score with respect to the first range of scores and second range of scores. The processing circuit also combines a normalized first risk score with a normalized second risk score to form at least a component of a composite risk index. The first risk score and second risk score being specific to a common property.

Abstract: A method and apparatus for real time testing of automated valuation using various indicators of accuracy. These indicators are then weighted according to their value as indicators of accuracy using individualized weighting factors or an equation. A ranking is then computed based upon the factors and their weights. This method is preformed continuously, so as to achieve real-time or periodically updated automated valuation model accuracy rankings.

Abstract: In various embodiments, points from flood maps (e.g., geospatial flood risk zoning maps) may be used in generating a flood frequency versus flood elevation curve for reducing the uncertainty in the flood risk assessment. In some embodiments, geospatial flood elevation lines for flood elevation lines at different flood frequency levels may be defined based on elevation datasets where there are inconsistencies between the elevation datasets and flood maps that were generated. The flood frequency versus flood loss curve may be derived based on the computed flood frequency versus flood elevation curve, digital elevation datasets, and collected damage curve. In some embodiments, the flood risk rating may also be derived and a flood risk assessment report may be generated.