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Jersey City, NJ, United States

Opera Solutions, LLC is a technology and analytics company mainly focused on capturing profit growth opportunities emerging from big data. The firm uses a combination of machine learning science, advanced predictive analytics, technology, large-scale data management, and human expertise to build and deliver analytics solutions to large and mid-sized clients in a number of sectors, including financial services, health-care, capital markets, insurance, consumer goods, retail, manufacturing, distribution, and government. Opera Solutions delivers predictive analytics as a service, and offers a number of hosted, cloud-based solutions focused on solving specific business problems, e.g., predicting the behavior of individual consumers, stopping revenue leakage in hospitals, warning of threats to corporate security or brand health, etc. The company also works with a limited number of very large clients to create and deliver more highly customized solutions. Wikipedia.

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.

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.

A system and method for grouping medical codes for clinical predictive analytics is provided. The system for predictive modeling using medical information comprising a computer system for electronically receiving a data set of medical diagnosis codes and applying indicator variables to the data set, the computer system allowing a user to define a target and one or more thresholds conditions, a supervised variable grouping engine executed by the computer system, said engine calculating, for each indicator variable, a vector length and a distance to a target vector, wherein each indicator variable initially forms a group, automatically combining two groups of indicator variables that satisfy threshold conditions to create a combined group, recalculating the combined groups vector length, distance to the target vector, and distance to vectors of other remaining groups, iteratively combining and recalculating until there are no two groups that satisfy the threshold conditions or until a satisfactory number of groups is formed; and generating an altered data set of medical code groupings with reduced dimensionality and inputting the altered data set into a predictive model.

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. More specifically, provided is a system comprising a computer executing a price sensitivity engine and a price aggregation engine, the price sensitivity engine receiving time-series information, determining covariate coefficients to estimate a population price sensitivity average, modeling a first set of vectors based on the covariate coefficients, modeling a second set of vectors based on the covariate coefficients and an indicator variable, and estimating sub-population price sensitivities based on the first and second sets of vectors; and the price aggregation engine comparing each of the sub-population price sensitivities to the population price sensitivity average and/or to other sub-population price sensitivities, ranking, ordering, and/or clustering the sub-populations, and determining aggregate price adjustments to items in the sub-populations.

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.

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