Abstract: A system and method for data anonymization using hierarchical data clustering and perturbation is provided. The system includes a computer system and an anonymization program executed by the computer system. The system converts the data of a high-dimensional dataset to a normalized vector space and applies clustering and perturbation techniques to anonymize the data. The conversion results in each record of the dataset being converted into a normalized vector that can be compared to other vectors. The vectors are divided into disjointed, small-sized clusters using hierarchical clustering processes. Multi-level clustering can be performed using suitable algorithms at different clustering levels. The records within each cluster are then perturbed such that the statistical properties of the clusters remain unchanged.

Abstract: Provided is a system for detecting a merchant point of compromise. More specifically, provided is a system for detecting a merchant point of compromise comprising a computer system in electronic communication with a transaction processing network containing transaction information, the computer system comprising a point-of-compromise detector, said point-of-compromise detector performing the steps of electronically receiving from the transaction processing network the transaction information; generating at least one of an undirected network or a directed network based on the transaction information; extracting features from the at least one of the undirected network or the directed network; and identifying one or more point-of-compromise merchants based on the extracted features.

Abstract: A system and method for generating greedy reason codes for computer models is provided. The system for generating greedy reason codes for computer models, comprising a computer system for receiving and processing a computer model of a set of data, said computer model having at least one record scored by the model, and a greedy reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to identify reason code variables that explain why a record of the model is scored high by the model, and build an approximate model to simulate a likelihood of a high score being generated by at least one of the reason code variables identified by the engine.

Abstract: A system and method for healthcare outcome predictions using medical history categorical data is provided. The system for healthcare outcome predictions using medical history categorical data comprising a computer system for receiving medical history categorical data, a healthcare outcome prediction engine stored on the computer system which, when executed by the computer system, causes the computer system to process the medical history categorical data to define a set of high-level constructs, calculate smoothed and thresholded Weight of Evidence tables for each high-level construct using training data, calculate an Evidence Ranked Sum value for each instance of each high-level construct based on the Weight of Evidence tables, and build predictive models based on the calculated Evidence Ranked Sum values.

Abstract: Provided is a system for estimating price sensitivities and determining aggregate price adjustments for a population of items, the population comprising a plurality of sub-populations.

Abstract: A system and method for grouping medical codes for clinical predictive analytics is provided.

Abstract: A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code.

Abstract: A system and method for developing proxy models is provided. The system for developing proxy models comprising a proxy model development computer system in electronic communication with a training database storing training data therein, and a plurality of computer models including a complex model and a proxy model that are trained by the computer system using the training data from the training database, wherein the computer system evaluates performance of each of the plurality of computer models, and if the computer system determines that the proxy model at least meets pre-defined performance criteria and approximates performance of the complex model, then the computer system communicates to a user that the proxy model can substitute the complex model.

Abstract: A system and method for optimizing collections processing is provided.

Abstract: An adaptive method for estimating the selling price for an item at auction in order to set a reserve. The method calculates the selling price as a function of selling prices for items previously-sold at auction and differential values attributable to feature differences between the item to be sold and comparative items previously sold. Distance metrics are calculated by comparing the item to be sold with each item in the set of comparative items, and a subset of most similar items is selected according to the calculated distance metrics. A weighting function is then calculated for each item in the subset based on its respective distance metric value, and the selling price is estimated as a function of the weighting functions and the differential values. The differential values are modeled as a linear dynamical system and updated using a Kalman filter as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the differential values.

Abstract: A system and method for data mining using domain-level context is provided. The system includes a computer system and a contextual data mining engine executed by the computer system. The system mines and analyzes large volumes of open-source documents/data for analysts to quickly find documents of interest. Documents/data are encoded into an ontological database and represented as a graph in the database linking contextual entities to find patterns and anomalies in context. Documents are separately analyzed by the system and scored on several different scales. The resulting information could be presented to the user via a visualization interface which allows the user to explore the data and quickly navigate to documents of interest.

Abstract: Exemplary embodiments of the present disclosure are related to systems, methods, and computer-readable medium to facilitate modifying a distribution of data elements to more closely resemble a reference distribution. In exemplary embodiments a modification constraint can be assigned to limit a modification of data elements in a subject distribution and a reference distribution can be identified. Data elements in the subject distribution can be programmatically modified to generate a modified distribution based on a reference distribution, wherein a modification of the data elements can be constrained in response to the modification constraint.

Abstract: A system and method for threat assessment are provided. The system includes a computer system; a data access subsystem programmed into and executed by the computer system for receiving data from a data source, and parsing the data to identify one or more messages; an analytics subsystem programmed into and executed by the computer system for processing and scoring the one or more messages to calculate one or more threat scores; and a communications subsystem programmed into and executed by the computer system for transmitting the one or more messages to a user.

Abstract: A method, system and non-transitory computer-readable medium for predicting customer attrition are provided. The method can include executing code to load historical data relating to a customer into a database to create a customer history summary file. The method can include executing code to load daily data relating to the customer into a scoring engine. The method can include programmatically processing, via the scoring engine, at least one of the historical data and the daily data to generate an attrition score. The attrition score can indicate a predictive signal of attrition of the customer.

Abstract: Aspects of the present disclosure are directed to systems and methods that facilitate the recommendation of an item to a user. One particular aspect includes a method including the steps of: a) receiving a user request for a new recommendation including rating information for previously-selected items, b) retrieving a mean vector and a covariance matrix representing estimates of prior ratings for a plurality of items by other users, c) calculating a selection likelihood statistics as a function of the estimated mean vector, the estimated covariance matrix and the rating information provided by the, d) calculating ranking statistics as a function of the selection likelihood statistics, and e) transmitting a response to the user recommending at least one of the items not previously selected as a function of the ranking statistics. The recommended item may, for example, be a movie.

Abstract: A system and method for recommending items to a user is provided. The system could combine recommendations provided by multiple recommenders by: a) calculating for each recommender j a maximum score Pj for the recommended n items as a function (e.g., sum) of stored recommender ratings for the n items, b) calculating a minimum acceptable score for each recommender system j as a function of the maximum score Pj and a predetermined tradeoff factor ?j such that the minimum acceptable score for at least one recommender system j is less than the maximum score Pj, c) selecting at least one set of items from the plurality of items, such that scores Pj (and/or sum of scores Pj) calculated for the selected set of items for each recommender system j are greater than the respective minimum acceptable score for that recommender system j, and d) identifying selected set of items to the user.

Abstract: A system and method for secure document distribution is provided. The system includes a computer system and a secure document distribution engine. The system includes a two-factor authentication system that includes a password and a hardware component. Documents can be accessed from a network (e.g., the Internet, a cloud computing resource, etc.), via a link as an e-mail attachment, or as a stored file. Redistribution of documents by malicious authorized users is not possible without attribution due to the view-only nature of the system in combination with other measures that include event logging and document watermarking. Access can be revoked or blocked in real time, regardless of how the files were distributed or where they reside.

Abstract: A system and method for matching data using probabilistic modeling techniques is provided. The system includes a computer system and a data matching model/engine. The present invention precisely and automatically matches and identifies entities from approximately matching short string text (e.g., company names, product names, addresses, etc.) by pre-processing datasets using a near-exact matching model and a fingerprint matching model, and then applying a fuzzy text matching model. More specifically, the fuzzy text matching model applies an Inverse Document Frequency function to a simple data entry model and combines this with one or more unintentional error metrics/measures and/or intentional spelling variation metrics/measures through a probabilistic model. The system can be autonomous and robust, and allow for variations and errors in text, while appropriately penalizing the similarity score, thus allowing dataset linking through text columns.

Abstract: A system and method for detecting network intrusions using one or more statistical models and a generalized likelihood ratio test (GLRT) is provided. The system includes a computer system and a network intrusion detection engine executed by the computer system. To detect network intrusions, the system receives network traffic data, computes a likelihood using one or more statistical models, such as an Markov-modulated Poisson process, and processes the traffic data using a GLRT. The statistical models are used to assess the likelihood of seeing a particular pattern of network traffic. The GLRT is used to classify a particular pattern as either indicative of an attack or not indicative of an attack. The system could apply one or more types of statistical models, such as in a flexible multi-tiered approach.

Abstract: A system and method for demand forecasting is provided. The system includes a computer system and a demand forecasting engine executed by the computer system. The demand forecasting engine permits inventory management and price optimization, with improved prediction accuracy. The system includes a nonlinear time series model which is able to simulate the market trend of a certain product in the past and make a demand forecast for the future. The system provides initial estimates for new products in a new season, and is self-adjusting with limited data points in the beginning of the season. In this way, the system provides solutions for demand forecasting of old and new products. Furthermore, pricing optimization solutions (e.g., Markov decision processed (MDP) based, etc.) can be built on the basis of the demand forecasting solutions. The system is applicable to many marketing-related problems and is particularly reliable for products which follow a certain cycle.