Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.

Abstract: Various embodiments are generally directed to techniques for utilizing disruptions to enterprise systems, such as to test and/or improve the ability of the enterprise system to recover from system failures, for instance. In many embodiments, an enterprise system may include two or more networked components, such as hardware components and software components. Some embodiments are particularly directed to generating a disruption scheme for an enterprise system based on analysis of one or more aspects of the enterprise system. For example, embodiments may include one or more of planning, scheduling, creating, timing, implementing, administering, and/or strengthening against a disruption to an enterprise system in a controlled and monitored manner. In many embodiments, administration of a disruption scheme may be monitored, recorded, and/or analyzed. In many such embodiments, a library of disruption events may be generated based on monitoring, recording, and/or analyzing implementation of the disruption scheme.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.

Abstract: In some implementations, a physical medium for fingerprint-based PIN entry may include a fingerprint scanner, one or more memories that store fingerprint data and a PIN, and one or more processors. The physical medium may receive power from a terminal that is in communication with the physical medium. The physical medium may scan a fingerprint using the fingerprint scanner and the power received from the terminal. The physical medium may compare scanned fingerprint data, determined based on scanning the fingerprint, and the stored fingerprint data using the power received from the terminal. The physical medium may determine that the scanned fingerprint data sufficiently matches the stored fingerprint data based on comparing the scanned fingerprint data and the stored fingerprint data. The physical medium may transmit the PIN to the terminal based on determining that the scanned fingerprint data sufficiently matches the stored fingerprint data.

Abstract: In some implementations, a system may receive, from a terminal, an account identifier, an authentication credential, and a request for data associated with a user, wherein the request may identify requested data items associated with the account identifier. The system may authenticate the request using the account identifier and the authentication credential. The system may determine whether the request satisfies a pre-approval condition after authenticating the request. The system may transmit a first message to a user device associated with the user or transmit a second message to a third party device associated with the terminal based on whether the requested data items satisfy the pre-approval condition. The first message to the user device may request approval to transmit the requested data items to the third party device via the second message, and the second message may enable access to the requested data items.

Abstract: Various embodiments are generally directed to techniques for utilizing disruptions to enterprise systems, such as to test and/or improve the ability of the enterprise system to recover from system failures, for instance. In many embodiments, an enterprise system may include two or more networked components, such as hardware components and software components. Some embodiments are particularly directed to generating a disruption scheme for an enterprise system based on analysis of one or more aspects of the enterprise system. For example, embodiments may include one or more of planning, scheduling, creating, timing, implementing, administering, and/or strengthening against a disruption to an enterprise system in a controlled and monitored manner. In many embodiments, administration of a disruption scheme may be monitored, recorded, and/or analyzed. In many such embodiments, a library of disruption events may be generated based on monitoring, recording, and/or analyzing implementation of the disruption scheme.

Abstract: Various embodiments are generally directed to techniques for network strengthening, such as by detecting issues with one or more network components and reconfiguring one or more upstream or downstream network components to preempt issues with the one or more upstream or downstream network components, for instance. Some embodiments are particularly directed to a tool (e.g., strengthening agent) that implements pre-scripted or dynamic hardening of up and downstream dependencies of a network component in response to an issue identified with the network component. In many embodiments, up and downstream components of a network component may be reconfigured while the issue with the network component is being addressed to preempt issues with the up and downstream components.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.

Abstract: Various embodiments are generally directed to techniques to perform transaction with transaction cards utilizing characteristics of customer's signature. Techniques may further include determining characteristics of a signature including a reduced set of data points for the signature and storing the characteristics in secure memory of transaction card.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Techniques to utilize excess resources in a cloud system, such as by enabling an auxiliary resource utilizer to use resources while they are not needed to support primary resource utilizers, are described herein. Some embodiments are directed to identifying and allocating excess capacity of resources in a cloud system to auxiliary resource utilizers based on one or more policies. In various embodiments, excess resources in one or more of the set of resources in the cloud system, or cloud resources, may be determined based on monitoring utilization of the cloud resources by the primary resource utilizers. In many embodiments, an auxiliary resource utilizer that is in compliance with a set of utilization policies may be identified and the excess resources may be allocated to the auxiliary resource utilizer.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for transition-coded media, measuring engagement of transition-coded media, and distribution of components of transition-coded media. For instance, the method may include: obtaining a media file; determining whether the media file includes transition-coded media; in response to determining the media file includes the transition-coded media, setting up a dynamic media environment; detecting whether a user is interacting with the transition-coded media; and in response to detecting the user is interacting with the transition-coded media, executing the transition-coded media with the dynamic media environment.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.