Abstract: A growth predictor includes a monitor, a prediction engine, and a prioritization engine. The monitor receives or generates first information of a network already identified as a candidate money laundering (ML) network by an anti-money-laundering system. The prediction engine predicts second information indicative of a growth size of the ML network at a future time based on the first information. The prediction engine executes one or more predictive models to generate the second information indicative of growth size based on the first information, which indicates one or more changes that have occurred in the candidate ML network over a past period of time. The prioritization engine determines a priority of the candidate ML network based on the second information.

Abstract: Embodiments provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques. Method performed by server system includes accessing financial data items and news items associated with finances of business entities from data sources for particular time duration. Method includes generating financial and news feature vectors corresponding to business entities and applying machine learning models over financial feature vectors and news feature vectors associated with business entities for determining candidate set of business entities predicted to be engaged in business acquisition in future. Method includes creating dynamic bipartite knowledge graph for each distinct time durations within particular time duration and generating static bipartite knowledge graph based on dynamic bipartite knowledge graphs for distinct time durations.

Abstract: Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Abstract: The present invention is for multi-screen session mobility between terminal groups. The invention uses a set of screen groups and a set of terminal groups to perform a mapping of the screen groups onto the terminal groups. Both the screen groups and the terminal groups have a geometry, which in one embodiment is defined by columns and rows. In cases where the number of columns in the screen group is less then or equal to the number of columns in the terminal group and where the number of rows in the screen group is also less then or equal to the number of rows in the terminal group, a first mapping scheme is employed. In cases where the number of columns in the screen group exceeds the number of columns in the terminal group or the number of rows in the screen group exceeds the number of rows in the terminal group, a second mapping scheme is employed. In one embodiment, the first mapping scheme maps the screen group directly onto the terminal group.

Abstract: The present invention is for multi-screen session mobility between terminal groups. The invention uses a set of screen groups and a set of terminal groups to perform a mapping of the screen groups onto the terminal groups. Both the screen groups and the terminal groups have a geometry, which in one embodiment is defined by columns and rows. In cases where the number of columns in the screen group is less then or equal to the number of columns in the terminal group and where the number of rows in the screen group is also less then or equal to the number of rows in the terminal group, a first mapping scheme is employed. In cases where the number of columns in the screen group exceeds the number of columns in the terminal group or the number of rows in the screen group exceeds the number of rows in the terminal group, a second mapping scheme is employed. In one embodiment, the first mapping scheme maps the screen group directly onto the terminal group.