Abstract: Various techniques are described that enable a smaller insurer (or an insurer with a less developed dataset) to be able to characterize whether certain healthcare insurance claim elements are potentially fraudulent or erroneous. Datasets from larger insurers (with well developed datasets) and/or datasets from a consortium of insurers can be leverage by the smaller insurer. Related techniques, apparatus, systems, and articles are also described.

Abstract: Data characterizing a plurality of related action graphs is received. Thereafter, the plurality of related action graphs are transformed into a logically equivalent structure so that rendering of the logically equivalent structure can be initiated. Related interfaces, apparatus, systems, techniques and articles are also described.

Abstract: Techniques are described for logically comparing strategies. In one aspect the strategies can be compared by receiving a request to compare a first strategy to a second strategy, the first strategy graphically represented by a first set of linked nodes, the second strategy graphically represented by a second set of linked nodes, each set of linked nodes linking a root node to at least one action node; identifying a subset of linked nodes from at least one of the first set of linked nodes and the second set of linked nodes based on an equivalence of a first subset of the first set of linked nodes to a second subset of the second set of linked nodes; and, providing a visual depiction of the identified subset of the linked nodes to a user, the visual depiction corresponding to the equivalence of the first subset to the second subset.

Abstract: Data characterizing a plurality of sensor generated events is received. Thereafter, analysis of the plurality of events is initiated using a decision tree with splits performed on decision keys. A first portion of the decision keys comprising analyst-selected splits can be derived from user-generated domain knowledge regarding a first plurality of historical events. A second portion of the decision keys comprising software-driven splits can be derived from a predictive model trained using a second plurality of historical events. Later, a disposition is determined for each event based on a traversal of at least one of the decision keys in the decision tree and such disposition is later initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: The invention aids an entity operating on the Internet or on another network to selectively request additional data about a user who has made a request for an interaction with the entity. The invention helps an entity to determine when and how to request additional data so as to reduce the likelihood of causing the user to have an adverse reaction, e.g., terminate the interaction. One embodiment of the invention concerns customers requesting transactions with on-line merchants. More specifically, this embodiment aids merchants by detecting Internet credit card transactions that are likely to be fraudulent, and providing the merchants with mechanisms for managing a suspected transaction as it occurs to obtain additional information that can be useful to reducing the likelihood of fraud.

Abstract: A system and method of revenue assurance for a service activity are presented. Multiple data feeds are received by a revenue assurance analytic system, where each data feed contains the same logical component of the service activity. The multiple data feeds are processed to detect revenue assurance issues in the service activity based on one or more common linking keys between disparate records related to the logical component.

Abstract: The current subject matter provides the ability to infer a richer customer profile using purchase transaction data in conjunction with various hierarchical groupings of products as well as an ability to characterize products such that they can be used to enrich customer profiles. Related apparatus, systems, techniques and articles are also described.

Abstract: A system for classifying a transaction as fraudulent includes a training component and a scoring component. The training component acts on historical data and also includes a multi-dimensional risk table component comprising one or more multidimensional risk tables each of which approximates an initial risk value for a substantially empty cell in a risk table based upon risk values in cells related to the substantially empty cell. The scoring component produces a score, based in part, on the risk tables associated with groupings of variables having values determined by the training component. The scoring component includes a statistical model that produces an output and wherein the transaction is classified as fraudulent when the output is above a selected threshold value.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: Predicting impact of future actions on subsequent creditworthiness involves developing a prediction model that predicts a statistical interaction of performance expectation with likely post-scoring behavior. Including sensitivity to new, post-scoring date credit behaviors in the analytic solution greatly improves snapshot score predictions. The modeling approach involves multiple snapshots: predictive and performance snapshots, plus an intermediate snapshot shortly after the predictive snapshot to quantify interim consumer behavior post-scoring date. Predictive interaction variables are calculated on the predictive data using simulated consumer profiles before and after assuming a sizeable simulated balance to infer the consumer's tolerance for incremental future debt. Using an adjustor approach in predicting capacity allows isolation of the confounding effect of risk from the capacity determination.

Abstract: A computer-implemented method described for making a recommendation with respect to a plurality of items using a plurality of adaptive models or agents. The described method includes receiving one or more business rules; receiving a request for a recommendation; receiving attributes relating to the request; activating one or more adaptive agents such that each activated adaptive agent generates one or more recommendations with respect to the items based at least in part on an evaluation of prior outcomes relating to the items; selecting from among the one or more recommendations at least one final recommendation; and displaying the at least one final recommendation to a user. Related apparatus, systems, techniques and articles are also described.

Abstract: A computerized method for detecting fraud includes obtaining frequency information on entities in transaction data for at least one individual account, converting frequency information to a frequency variable, and predicting whether an activity is fraudulent in response to the frequency variable. In some embodiments, the frequency variable is used with at least one other variable to predict fraudulent activity.

Abstract: The invention comprises a method and apparatus for explaining credit scores, for example in connection with a credit score explanation service, in which consumers can identify the sources of information used to establish their credit score, supply their credit report and credit score information in connection with their application for credit-related products and services, such as loans, and determine the effect on their credit score and cost for credit-related products and services based upon various hypothetical changes in their credit behavior.

Abstract: In one aspect, input data for a predictive model characterizing a level of risk for a data transaction is received that includes values for categorical variables and one or more of binary variables and continuous variables the predictive model. Thereafter, one or more of the categorical variables is associated with one of a plurality of keys. Each key having corresponding coefficients for at least a subset of the binary variables and the continuous variables and the coefficients being dependent on a value for the key. A composite value based on values for each of at least a subset of the binary variables and the continuous variables as calculated using the corresponding coefficients for each key can then be generated. Scoring of the data transaction using the binary variables, the continuous variables, and the composite variables can then be initiated by the predictive model. Related apparatus, systems, techniques and articles are also described.

Abstract: A system and method for maintaining a pre-defined score distribution for financial transactions are disclosed. A number of memory spaces are defined for a memory structure. Transaction data for the financial transactions is received by the system. Each of the financial transactions is scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, where each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score of the batch of scores in an associated memory space of the plurality of memory spaces, and a percentile is computed for each score in the batch of scores based on a set of values associated with the count of each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values.

Abstract: Transaction processing of online transactions at merchant sites determines the likelihood that such transactions are fraudulent, accounting for unreliable fields of a transaction order, which fields do not reliably identify a purchaser. A scoring server using statistical model uses multiple profiles associated with key fields, along with weights to indicate the degree to which the profiles identify the purchaser of the transaction.

Abstract: Various techniques are described that enable a smaller insurer (or an insurer with a less developed dataset) to be able to characterize whether certain healthcare insurance claim elements are potentially fraudulent or erroneous. Datasets from larger insurers (with well developed datasets) and/or datasets from a consortium of insurers can be leverage by the smaller insurer. Related techniques, apparatus, systems, and articles are also described.

Abstract: A system and method for detecting fraud is presented. A self-calibrating outlier model is hosted by a computing system. The self-calibrating outlier model receives transaction data representing transactions, and is configured to calculate transaction-based variables, profiles and calibration parameters, and to produce a score based on the transaction data according to the transaction-based variables, profiles and calibration parameters. An adaptive cascade model is also hosted by the computing system, and is configured to generate a secondary score for the transaction data based on profile information from the variables and/or profiles calculated by the self-calibrating outlier model, and based on a comparison with labeled transactions from a human analyst of historical transaction data.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Abstract: An improved method and mechanism for specified temporal deployment of rules within a rules server. A rules server applies a set of rules, stored within a rulebase, to a given set of input values or parameters. Each rule is associated with a start time and an end time. Upon receiving a transaction request, a transaction time is determined for the transaction request. Subsequently, a set of effective rules within the ruleset are identified by the rules server, wherein each effective rule has a start time before the transaction time, and an end time that is after the transaction time. The rule server applies only the set of effective rules to the transaction.