Abstract: Systems, computer program products, and methods are described herein for intrusion detection using resource activity analysis. The present invention is configured to receive, from a computing device of a user, an indication that the user has accessed a resource allocation portfolio of a customer; determine a geographic information of the user; retrieve a geographic information of the customer; determine that the geographic information of the user does not match the geographic information of the customer; determine an exposure level associated with the user access of the resource allocation portfolio of the customer; determine that the exposure level is greater than a predetermined threshold; and automatically trigger a transmission of a notification to a computing device of an administrator indicating that the exposure level associated with the user access of the resource allocation portfolio of the customer is greater than the predetermined threshold.

Abstract: Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.

Abstract: The present invention is generally related to systems and methods for providing an improved authentication and verification system through the use of compiled user data and user location or traffic data from multiple channels of input. Multiple devices may be utilized by the system in order to receive and process data to authenticate user identities and verify the validity of account activity.

Abstract: The present invention is generally related to systems and methods for providing an improved authentication and verification system through the use of compiled user data and user location or traffic data from multiple channels of input. Multiple devices may be utilized by the system in order to receive and process data to authenticate user identities and verify the validity of account activity.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Systems, computer program products, and methods are described herein for intrusion detection using resource activity analysis. The present invention is configured to receive, from a computing device of a user, an indication that the user has accessed a resource allocation portfolio of a customer; determine a geographic information of the user; retrieve a geographic information of the customer; determine that the geographic information of the user does not match the geographic information of the customer; determine an exposure level associated with the user access of the resource allocation portfolio of the customer; determine that the exposure level is greater than a predetermined threshold; and automatically trigger a transmission of a notification to a computing device of an administrator indicating that the exposure level associated with the user access of the resource allocation portfolio of the customer is greater than the predetermined threshold.

Abstract: Systems, computer program products, and methods are described herein for intrusion detection using resource activity analysis. The present invention is configured to receive, from a computing device of a user, an indication that the user has accessed a resource allocation portfolio of a customer; determine a geographic information of the user; retrieve a geographic information of the customer; determine that the geographic information of the user does not match the geographic information of the customer; determine an exposure level associated with the user access of the resource allocation portfolio of the customer; determine that the exposure level is greater than a predetermined threshold; and automatically trigger a transmission of a notification to a computing device of an administrator indicating that the exposure level associated with the user access of the resource allocation portfolio of the customer is greater than the predetermined threshold.

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

Abstract: The present invention is generally related to systems and methods for providing an improved authentication and verification system through the use of compiled user data and user location or traffic data from multiple channels of input. Multiple devices may be utilized by the system in order to receive and process data to authenticate user identities and verify the validity of account activity.

Abstract: Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

Abstract: Systems, computer program products, and methods are described herein for intrusion detection using resource activity analysis. The present invention is configured to receive, from a computing device of a user, an indication that the user has accessed a resource allocation portfolio of a customer; determine a geographic information of the user; retrieve a geographic information of the customer; determine that the geographic information of the user does not match the geographic information of the customer; determine an exposure level associated with the user access of the resource allocation portfolio of the customer; determine that the exposure level is greater than a predetermined threshold; and automatically trigger a transmission of a notification to a computing device of an administrator indicating that the exposure level associated with the user access of the resource allocation portfolio of the customer is greater than the predetermined threshold.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Aspects of the disclosure relate to detection of unauthorized usage in debit card transactions using a transaction management computing platform and an analytics computing platform. A computing platform may monitor a plurality of transactions at an automated teller machine. Subsequently, the computing platform may identify at least one unusual activity in the plurality of transactions at the automated teller machine. In response to identifying the at least one unusual activity in the plurality of transactions, the computing may analyze each account corresponding to the plurality of transactions to identify a common point of purchase for a subset of accounts. Thereafter, the computing platform may flag the subset of accounts for unauthorized usage.