Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.

A computer system measures trust of an entity in electronic documents from electronic media sources is described. A communication network is linked to one or more of the sources. A computer server is in communication with the communication network and is configured to receive electronic documents via the communication network. The computer server having a memory and a processor accessing a database. The memory includes processor executable instructions stored in the memory and executable by the processor. The computer executable instructions comprise preliminary term vector instructions, calculating instructions for determining the preliminary term vectors in the received electronic documents, and refined term vector instructions for defining a plurality of industry-specific term vectors.

Embodiments predict future demand for a first product by receiving historical sales data for an aggregate products/locations level, the historical sales data including a plurality of sales data points, including sales data points for the first product at each of a plurality of locations. Embodiments extract a plurality of different types of features related to sales of each of the products and generate a plurality of clusters of sales data points based on the plurality of different types of features. Embodiments train each of the clusters to generate a plurality of trained cluster models including promotion effects per cluster. For a particular time period, a particular location and the first product, embodiment identify the features for the time period and map to one of the trained cluster models to fetch the promotion effects for the time period. Embodiments then use the promotion effects to forecast demand for the first product.

A method for automatically populating an ecommerce cart is disclosed. The method includes receiving a request to add a product item to a shopping cart of an ecommerce shopping environment. The method includes adding the product item to the shopping cart. The method includes updating, responsive to adding the product item, a shopping cart status. The method includes determining a failure of the shopping cart status to satisfy a purchasing criteria associated with the ecommerce shopping environment. The method includes selecting, responsive to determining the failure, an additional product item based on information associated with an user account. The method includes adding the additional product item to the shopping cart.

According to one embodiment of the present invention, a system for modifying content associated with an item comprises at least one processor. Features of interest of the item to a plurality of different groups are determined based on user comments produced by members of the plurality of different groups. The members within each group have a common characteristic. The features of interest to each group within the content associated with the item are identified, and the content associated with the item is modified by balancing the features of interest to the plurality of different groups within the content associated with the item. Embodiments of the present invention further include a method and computer program product for modifying content associated with an item in substantially the same manner described above.

According to one embodiment of the present invention, a system for modifying content associated with an item comprises at least one processor. Features of interest of the item to a plurality of different groups are determined based on user comments produced by members of the plurality of different groups. The members within each group have a common characteristic. The features of interest to each group within the content associated with the item are identified, and the content associated with the item is modified by balancing the features of interest to the plurality of different groups within the content associated with the item. Embodiments of the present invention further include a method and computer program product for modifying content associated with an item in substantially the same manner described above.

Systems and methods for the calculation of a dynamic trust score are disclosed. The dynamic trust score may indicate a likelihood that the consumer will complete the transaction in a positive manner. The system may calculate the dynamic trust score based on various static and dynamic variables including digital identity data, internal data, third-party data, private data, and/or data from the transaction initiated by the consumer.

Systems and methods for the calculation of a dynamic trust score are disclosed. The dynamic trust score may indicate a likelihood that the consumer will complete the transaction in a positive manner. The system may calculate the dynamic trust score based on various static and dynamic variables including digital identity data, internal data, third-party data, private data, and/or data from the transaction initiated by the consumer.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of keystone activity based suggestions. The innovation detects a keystone activity of a user. The keystone activity is a planned event for the user such as recently purchased tickets to a specific show. Customer data of a financial institution is accessed where the customer data includes data of customers of the financial institution. A set of similar customers to the user is determined. Transaction data of the set of similar customers is determined and analyzed for likelihood of the user wanting to attend a secondary activity that is similar the set of similar customers. The secondary activity can be automatically scheduled for the user based on the keystone activity and the transaction data.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of keystone activity based suggestions. The innovation detects a keystone activity of a user. The keystone activity is a planned event for the user such as recently purchased tickets to a specific show. Customer data of a financial institution is accessed where the customer data includes data of customers of the financial institution. A set of similar customers to the user is determined. Transaction data of the set of similar customers is determined and analyzed for likelihood of the user wanting to attend a secondary activity that is similar the set of similar customers. The secondary activity can be automatically scheduled for the user based on the keystone activity and the transaction data.