Jersey City, NJ, United States
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.

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A system and method for rapid development and deployment of reusable analytic code for use in computerized data modeling and analysis is provided. The system includes a centralized, continually updated environment to capture pre-processing steps used in analyzing big data, such that the complex transformations and calculations become continually fresh and accessible to those investigating business opportunities. The system incorporates deep domain expertise as well as ongoing expertise in data science, big data architecture, and data management processes. In particular, the system allows for rapid development and deployment of analytic code that can easily be re-used in various data analytics applications, and on multiple computer systems.


Patent
Opera Solutions | Date: 2014-01-06

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.


Patent
Opera Solutions | Date: 2014-01-27

A system and method for optimizing collections processing is provided. More specifically, the system for optimizing collections processing comprises a computer system in electronic communication with an accounts database storing customer and account information therein, the computer system executing a collections processing engine, said engine performing the steps of (a) electronically receiving a notification that an account has entered a collections process, (b) defining one or more customer behavior models based on customer and account information stored in the accounts database, (c) processing the customer and account information using the one or more models to calculate one or more model scores representing a probability that the account will pay to current while in a current collections stage having a treatment action associated therewith, (d) processing the calculated one or more model scores to calculate an optimal amount of time that the account should stay in the current collections stage, (e) determining whether to transfer the account to a next collections stage having another treatment action different than the current stage, and (f) repeating steps c-e until full payment is received and removing the account from the collections process.


Patent
Opera Solutions | Date: 2014-02-03

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.


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.


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.


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.


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.


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.

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