Abstract: Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.

Abstract: Methods for controlling genetic learning for predictive models using predefined strategies may include, for each of a plurality of agents, selecting a type of predictive model. A strategy may be selected from predefined strategies. Candidate genomes may be generated and may include a plurality of genes. Each gene may be associated with a feature of the agent predictive model. A fit of each candidate genome to the agent strategy may be determined. A candidate genome may be selected based on the fit. For each of a plurality of epochs, a plurality of training iterations may be performed for each agent. A fitness of each agent predictive model may be determined. A subset of agents with a highest fitness may be determined. For each agent of the subset, at least one new agent may be generated. The genomes of the new agents may be merged with some genomes of the subset.

Abstract: Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Abstract: Described herein are a system and techniques for enabling access control utilizing one or more blockchains associated with a user. A blockchain provider can manage one or more blockchains specifically associated with a an entity, where each blockchain may be associated with a differing sensitivity level. The entity may be a person or a machine such as an IOT (Internet of Things) device. The blockchain provider can manage access control policies associated with each blockchain such that access to the data of the blockchain may be allowed or restricted to requesting entities according to those access control policies.

Abstract: Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Abstract: A disclosed method includes a) receiving by a server computer network data comprising a plurality of transaction data for a plurality of transactions. Each transaction data comprises a plurality of data elements with data values. At least one of the plurality of data elements comprises a user identifier for a user. The server computer can then b) generate one or more graphs comprising a plurality of communities based on the network data. The server computer can c) determine fuzzy values for at least some of the data values for each transaction of the plurality of transactions. For each user, the server computer can d) determine fuzzy values for communities within the plurality of communities. The server computer can then e) generate a model using the fuzzy values obtained in steps c) and d), and at least some of the data values.

Abstract: A method includes a) receiving, by a computer, network data comprising a plurality of transactions conducted by a plurality of actual users and a plurality of actual resource providers. The computer can b) generate a plurality of simulated users. Each simulated user based upon a set of the plurality of actual users. The computer can then c) generate a plurality of simulated resource providers. Each simulated resource provider based upon at least one actual resource provider. The computer can d) execute a simulation using the plurality of simulated users and the plurality of simulated resource providers, and in response to step d) can e) determine a plurality of simulated transactions conducted by the plurality of simulated users and the plurality of simulated resource providers.

Abstract: Systems and methods are provided for transcompiling non-distributed source code for a non-distributed software program into a distributed software package for implementation on a distributed computing system. A transcompiler can identify loops within non-distributed source code written in a data-driven language. The transcompiler can generate MapReduce jobs using mapper keys based on grouping indicators associated with each of the loops. The MapReduce jobs can be linked together based on input-output connections of the loops in the non-distributed source code. Then, the transcompiler can generate a distributed software package including the generated MapReduce jobs to implement the same functionality as the non-distributed source code on the distributed computing system, thereby improving the speed of execution over very large datasets. The distributed software package can be optimized using machine learning searching algorithms.

Abstract: A method is disclosed and includes receiving, by a first communication device, a first local authentication model, the first local authentication model being derived from a master authentication model at a remote server computer, and receiving a request to perform an interaction with a second communication device, the interaction being performed in an offline manner. The method may further include applying, by the first communication device, the first local authentication model to the interaction to determine a first authentication result and determining whether or not to allow the interaction to proceed based upon the first authentication result. The method may also include updating the first local authentication model using the master authentication model when the first communication device is online.

Abstract: A computer-implemented method of using past transaction declines to predict future fraudulent behavior. A transaction has been declined for an account is determined, and a risk score is determined. The risk score is compared to a risk threshold. The transaction is compared to one or more transactions in a transaction profile for a past fraudster in response to the risk score being determined to be over the risk threshold. A best fit of the transaction profiles of the past fraudster is determined and a measure of success for the best fit of the transaction profiles of the past fraudster is also determined. If the measure of success is over a threshold, the method updates profiles of past fraudsters based on the transaction to include the transaction that has been declined. The method predicts future fraudulent transactions and attempts to stop future fraudulent transactions based on the predicted future fraudulent transactions.

Abstract: A method is disclosed. A user device may determine, prior to joining a mesh network comprising a plurality of user devices, a first data security value. The user device may then communicate with the plurality of user devices to join the mesh network. The user device may receive a plurality of additional data security values from a plurality of proximate user devices. The user device may then determine an updated data security value based at least upon evaluating the plurality of additional data security values associated with the plurality of proximate user devices. The user device may allow or not allow the user device to perform an interaction based at least upon the updated data security value.

Abstract: Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.

Abstract: A system and method are described which attempt to connect merchants with phone numbers that are known to be associated with fraudulent users.

Abstract: A method includes a processing computer querying a data store for plurality of network data since a last epoch of data. The processing computer can generate matrices based on the plurality of network data and then perform tensor factorization on the matrices, to obtain latent values in the network data. The processing computer can then determine that the latent values satisfy a predetermined criterion. The processing computer can input the latent values into a model associated with the signal, wherein the model generates output data. The processing computer can then generate an alert comprising the output data and then transmit the alert to a remote server computer.

Abstract: A method includes a processing computer receiving a processing request message comprising user data from a remote server computer. The processing computer can then determine latent values associated with the processing request message based on the user data and a multiplex graph. The processing computer can then normalize the latent values based on a community group in the multiplex graph. The community group can include at least a part of the user data. The processing computer can transmit a processing response message comprising at least one normalized latent value to the remote server computer.

Abstract: Systems and methods are provided for transcompiling non-distributed source code for a nondistributed software program into a distributed software package for implementation on a distributed computing system. A transcompiler can identify loops within non-distributed source code written in a data-driven language. The transcompiler can generate MapReduce jobs using mapper keys based on grouping indicators associated with each of the loops. The MapReduce jobs can be linked together based on input-output connections of the loops in the non-distributed source code. Then, the transcompiler can generate a distributed software package including the generated MapReduce jobs to implement the same functionality as the non-distributed source code on the distributed computing system, thereby improving the speed of execution over very large datasets. The distributed software package can be optimized using machine learning searching algorithms.

Abstract: Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Abstract: A method is disclosed. A user device may determine, prior to joining a mesh network comprising a plurality of user devices, a first data security value. The user device may then communicate with the plurality of user devices to join the mesh network. The user device may receive a plurality of additional data security values from a plurality of proximate user devices. The user device may then determine an updated data security value based at least upon evaluating the plurality of additional data security values associated with the plurality of proximate user devices. The user device may allow or not allow the user device to perform an interaction based at least upon the updated data security value.

Abstract: A method includes collecting, by a first AI application of a first AI computer, data to include in a dataset. The first AI application of the first AI computer can include an offer for the dataset into an offering platform. The first AI application of the first AI computer can then receive a response to the offer from a second AI application of a second AI computer. The first AI application of the first AI computer can determine whether or not to provide the dataset to the second AI application of the second AI computer. The first AI application of the first AI computer can, based on the determination, provide the dataset to the second AI application of the second AI computer.

Abstract: Embodiments of the invention are directed to methods and devices for predicting interactions. One embodiment is directed to a method comprising receiving, by one or more computers, interaction data for a plurality of known interactions between resource providers and users, and creating a topological graph based on the plurality of known interactions. The method may further comprise determining, by the one or more computers, a plurality of communities to form a predictive model, and receiving a request for a prediction. In addition, the method may comprise applying the request to the predictive model, by the one or more computers, by identifying a community in the plurality of communities corresponding to the request, determining a node within the identified community, and providing information regarding the node as the requested prediction.