Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: In one aspect, the invention relates to a method to propagate relations between a first rule set and a second rule set wherein the first and second rule sets are invoked by a common workflow model. The method includes tracing paths forward through the workflow model from the first rule set to the second rule set. Enumerating relations that extend forward from the first rule set to the second rule set is another step in the method. Additionally, using multi-valued logic, calculating the effects to the relations of control flow through the workflow model from the first rule set to the second rule set, tracing paths backward through the workflow model from the first rule set to the second rule set, enumerating relations that extend backward form the second rule set to the first rule set, and using multi-valued logic, calculating the effects on the relations of control flow backwards through the workflow model from the second rule set to the first rule set are also steps in the method.

Abstract: The invention comprises an article and method for transforming a complex or large decision tree having multiple variables; multiple values for each variable; and, multiple outcomes for each combination of variables and their associated values, into a compact, efficient graphical representation to provided enhanced ease of use and interaction by a human user. More particularly, the invention comprises a computationally efficient method for transforming an input decision tree into an optimal compact representation by computing a particular ordering of variables in the decision tree that first leads to a Directed Acyclic Graph, or “DAG,” with a minimum number of nodes. The method then converts the DAG into an exception-based DAG, or “EDAG,” with exactly one exception, having an optimal, minimum number of nodes with increased comprehensibility for a user.

Abstract: A method and apparatus for leveraging the inherent massiveness of real-world data sets to solve the problems typically associated with graphing the data is provided. Three particular areas of concern are as follows: a high likelihood of containing instances of bad or corrupted data that could distort the graph; little or no documentation about the type of each variable; and the presence of arbitrarily encoded missing or special values. One embodiment of the invention provides a methodology for automatically selecting a graphing range with minimal scale distortion. Another embodiment of the invention provides a methodology for automatically choosing an appropriate graphing style. Another embodiment of the invention provides a methodology for automatically detecting and filtering special values in data.

Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: Detection of insurance premium fraud is provided by a predictive model, which uses derived variables to assess the likelihood of fraud for each policy. The predictive model produces a score, which is a measure of the likelihood of premium fraud or abuse. The predictive model is included in a system that accepts policies to be considered for scoring, selects which policies are appropriate for scoring, stores data about the policies in a database, uses the data to derive variables for the model, and processes and outputs the model scores and related information. A rule-based analysis, which detects specific inconsistencies in the data that are indicative of premium fraud, may also be part of the system. The model scores and red-flag indicators from the rule-based analysis may be further processed to provide customized output for users.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: A system and method for managing mass compromise of financial transaction devices is disclosed. A method includes maintaining a summary of a transaction history for a financial transaction device, and forming a device history profile based on the transaction history, the device history profile including predictive variables indicative of fraud associated with the financial transaction device. A method further includes generating a fraud score based on the predictive variables, the fraud score representing a likelihood that the financial transaction device is compromised will be used fraudulently.

Abstract: A method includes obtaining from a first financial organization first information relating to a first financial account indicative of financial performance for the first financial account, obtaining from a second financial organization independent of the first financial organization second information relating to a second financial account indicative of financial performance for the second financial account, and determining if the first financial account and the second financial account relate to a common customer.

Abstract: A decision engine is provided that integrates all components of a credit application process, including access to and manipulation of credit bureau data, credit scoring, credit decisioning, and matching the correct products, into a single application service provider (ASP) platform which is accessible through a series of Application Programming Interfaces (APIs).

Abstract: A system and method for integrating messages across multiple applications. The applications may be on one server or on several servers, and each application may service one or more e-clients. The system and method securely segregates the data for each e-client. Further, the system can be dynamically reconfigured by adding or deleting new messages and/or applications, without affecting the performance of the unchanged parts of the system. The various applications using the system need not be aware of the existence of the other applications.

Abstract: A system and method is provided that provides tools to consumers to help consumers understand their credit scores and how to take action to improve their credit scores. A system and method for each of and for a combination of a score estimating tool, a best action simulation tool, an easy error correction tool, and a score improvement tool are provided.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: An iterative approach to solving the optimization problem is provided. The invention provides an iteration of four basic operations; determining the segments, balancing the segments, expanding a segment, and solving the segment optimization. The method and apparatus can use any off-the-shelf linear programming (LP) solver, such as Dash Optimization Xpress, by Dash Optimization, during the solve operation. The size of the problem fed into the LP solver remains bounded and relatively small compared to the entire problem size. Thus, the algorithm can solve problems of several orders of magnitude larger. In one embodiment of the invention, the sampling and segmentation techniques are removed to where the problem is solved at the account-level. In the above cases, the solution is produced in a more cost-effective manner and the best possible return is achieved because the doubt of achieving a true global solution is removed.

Abstract: A knowledge base is first characterized by an association-grounded semantics collapsed language. In response to the receipt of a query of the knowledge base, the collapsed language is used to determine whether there is an indication that the knowledge base contains knowledge requested in the query. Thereafter, the collapsed language can be used to carry out a full search for the knowledge much more efficiently than would otherwise be possible. Related methods, apparatus, and articles are also described.

Abstract: Entity disambiguation resolves which names, words, or phrases in text correspond to distinct persons, organizations, locations, or other entities in the context of an entire corpus. The invention is based largely on language-independent algorithms. Thus, it is applicable not only to unstructured text from arbitrary human languages, but also to semi-structured data, such as citation databases and the disambiguation of named entities mentioned in wire transfer transaction records for the purpose of detecting money-laundering activity. The system uses multiple types of context as evidence for determining whether two mentions correspond to the same entity and it automatically learns the weight of evidence of each context item via corpus statistics. The invention uses multiple search keys to efficiently find pairs of mentions that correspond to the same entity, while skipping billions of unnecessary comparisons, yielding a system with very high throughput that can be applied to truly massive data.

Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.