Abstract: Systems and methods for using models to optimize suggestions for long-term and short-term goals are disclosed. In one aspect, a method may include receiving input data associated with promotion of a product to one or more target entities, and defining a plurality of temporal goals for attainment based at least in part on the input data. The plurality of temporal goals may be classified into at least two categories of different timescales. The method may further include providing the plurality of temporal goals and a set of constraints to an optimization model comprising an objective function, and using the optimization model to generate a set of multi-dimensional actions with predicted action values for at least a subset of the plurality of channels. The predicted action values can collectively maximize an impact value of the objective function while balancing a feasibility of the plurality of temporal goals with respect to one another.

Abstract: The present disclosure provides systems, methods, and computer program products for explaining decision models. An example method may comprise (a) using a decision model to predict an action that a sales representative should take to maximize a target variable, wherein the decision model comprises a plurality of sub-models comprising a channel affinity sub-model and a content affinity sub-model; and (b) applying an explainability model to the decision model to generate one or more predictors or drivers of the output of the decision model, wherein the one or more predictors or drivers (1) are features of the channel affinity sub-model and/or the content affinity sub-model and (2) provide an explanation of an effect of the action on the target variable.

Abstract: The present disclosure provides systems, methods, and computer program products for explaining decision models. An example method may comprise (a) generating one or more predictive models; (b) generating a decision model from the one or more predictive models by imposing (i) a set of operational constraints and (ii) a set of brand strategy rules on the predictive model; (c) using the decision model to determine one or more optimal actions for maximizing one or more target variables; and (d) applying explainability modeling to the decision model to generate an explanation model, wherein the explanation model is useable by one or more users to gain insights or an understanding into interactions within the decision model affecting the sales of the one or more products.

Abstract: The present disclosure provides a method for generating an index score. The method may comprise (a) obtaining data associated with a plurality of interactions between (i) a plurality of sales representatives and (ii) a customer; (b) for each sales representative: (i) processing the data to determine a plurality of components of the index score; (ii) applying a plurality of weights and standardizations to the plurality of components to generate a plurality of metrics; (iii) aggregating the plurality of metrics to generate the index score, wherein the index score is indicative of an effectiveness of the sales representative in interacting with the customer; (c) selecting, based on the index scores of the plurality of sales representatives, a sales representative of the plurality of sales representatives; and (d) displaying, on a graphical user interface of an electronic device of the sales representative, a recommended interaction between the sales representative and the customer.

Abstract: System and method for generating case-based data including receiving, input data describing an event of interest; if the input data is not in a format of a property graph then transforming the input data into a first property graph describing the event of interest and representing a first network, wherein the first property graph includes a plurality of network elements and properties of at least some of the plurality of network elements, wherein the network elements include entities and links describing relationships between the entities; changing a network element in the first property graph to create a second property graph of a new network; and using the second property graph as the case-based data. New properties may be generated for the entities and links.

Abstract: A computer implemented method and system for optimization of model parameters of at least one predictive model for detecting suspicious financial activity. The processor may select a reduced set of key indicators and corresponding scores to optimize from each of the at least one predictive model, each key indicator and corresponding score in the reduced set having an influence ranking above a predetermined influence ranking. The processor may select a best performing model candidate based on an evaluation of each reduced set of key indicators and corresponding scores. The processor may preform gradient-ascent optimization on the best performing model candidate and the at least one random model to generate a set of at least two new models for each of the best performing model candidate and the at least one random model. The processor may select the new model with the highest performance ranking.

Abstract: Method and system for configuring a model and deploying it on a target. The method may include using a processing unit, automatically generating a plugin based on a statistical model, the plugin including variables and issue definitions extracted from the model; and deploying the plugin on a target platform.

Abstract: System and method for generating case-based data including receiving, input data describing an event of interest; if the input data is not in a format of a property graph then transforming the input data into a first property graph describing the event of interest and representing a first network, wherein the first property graph includes a plurality of network elements and properties of at least some of the plurality of network elements, wherein the network elements include entities and links describing relationships between the entities; changing a network element in the first property graph to create a second property graph of a new network; and using the second property graph as the case-based data. New properties may be generated for the entities and links.

Abstract: A computer implemented method and system for optimization of model parameters of at least one predictive model for detecting suspicious financial activity. The processor may select a reduced set of key indicators and corresponding scores to optimize from each of the at least one predictive model, each key indicator and corresponding score in the reduced set having an influence ranking above a predetermined influence ranking. The processor may select a best performing model candidate based on an evaluation of each reduced set of key indicators and corresponding scores. The processor may preform gradient-ascent optimization on the best performing model candidate and the at least one random model to generate a set of at least two new models for each of the best performing model candidate and the at least one random model. The processor may select the new model with the highest performance ranking.

Abstract: A system or method may include receiving, by a processor, data describing a network, wherein the network includes a plurality of entities and links describing relationships between the plurality of entities. The method may further include identifying a set of seed entities from the plurality of entities based on predefined rules. The method may further include generating a set of sub-networks based on the set of identified seed entities, wherein each of the sub-networks may include one or more other entities of the plurality of entities having at least one link to the at least one seed entity. The method may further include calculating a risk score for each of the generated sub-networks.

Abstract: Method and system for configuring a model and deploying it on a target. The method may include using a processing unit, automatically generating a plugin based on a statistical model, the plugin including variables and issue definitions extracted from the model; and deploying the plugin on a target platform.

Abstract: A system and method for determining a risk root cause are provided. A first and second fraud related risk scores respectively associated with a first and second nodes may be determined. A relation strength value related to at least one relation between the first and second nodes may be determined. A relation strength value and a first and second node risk scores may be used to calculate a cluster risk score for a cluster including the first and second nodes. Other embodiments are described and claimed.

Abstract: A system and method for determining a risk root cause are provided. A first and second fraud related risk scores respectively associated with a first and second nodes may be determined. A relation strength value related to at least one relation between the first and second nodes may be determined. A relation strength value and a first and second node risk scores may be used to calculate a cluster risk score for a cluster including the first and second nodes. Other embodiments are described and claimed.

Abstract: A system and method for determining a risk root cause are provided. A first and second fraud related risk scores respectively associated with a first and second nodes may be determined. A relation strength value related to at least one relation between the first and second nodes may be determined. A relation strength value and a first and second node risk scores may be used to calculate a cluster risk score for a cluster including the first and second nodes. Other embodiments are described and claimed.