Abstract: Expedited virtual currency transactions are provided by identifying a first user primary wallet associated with a virtual currency and including a first user primary wallet private key. First user secondary wallets are created that each include a respective first user secondary wallet private key, and a respective virtual currency transaction is performed using the first user primary wallet private key to transfer predefined amounts of the virtual currency from the first user primary wallet to each of the first user secondary wallets such that first user secondary wallets are provided with different predefined amounts of the virtual currency. Subsequently, an instruction is received to transfer a payment amount to a second user, and the second user is allocated a subset of the first user secondary wallet private keys included in respective first user secondary wallets that are associated with predefined amounts of the virtual currency that equal the payment amount.

Abstract: Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.

Abstract: Techniques are disclosed in which an edge user computing device pre-processes a stream of user data prior to using the stream of data to train a machine learning model at the edge device. The edge device receives the stream of user data, where the stream of data includes a first set of characteristics associated with the edge device and a second set of characteristics associated with a plurality of user requests received from a user of the edge device. The edge device repeatedly generates, using the stream of data, sets of pre-processed user data by performing pre-processing techniques on characteristics included in the stream of data. The edge device repeatedly trains, using the sets of pre-processed data, a baseline model to generate a device-trained model, where the baseline model is trained at the edge device without providing user data included in the stream of data to a server computer system.

Abstract: Systems and methods for providing a merchant-customer engagement system include a physical merchant location in which a customer may be tracked to determine products within the physical merchant location that the customer is interested in. A merchant may use a wearable merchant device that is configured to identify the customer via a camera that captures an image of the customer, and that image may be used to retrieve customer account information for that customer. Using the customer account information and the products that the customer is showing interest in, the wearable merchant device may be used to display merchant-customer engagement information next to a view of the customer to allow the merchant to help the customer while utilizing a variety of displayed information about the customer including, for example, past purchases, products browsed in-store and previously browsed online, time spent in an area of the physical merchant location, etc.

Abstract: Methods and systems for a device identification system may be provided. The device identification system may determine an identity of a user device associated with a transaction. The identity may be determined by network address information, hard link information, soft link information, and/or other such information. The network address information may include IPv4 information, IPv6 information, a device ID, and/or other such information. The identity of the user device may be determined and a transaction conducted from the user device may be assigned a fraudulent transaction risk score according to the information. Transactions that are determined to be at a high risk of fraud may be reviewed or otherwise flagged and/or canceled.

Abstract: Methods and systems for detecting malware by monitoring client-side memory stacks are described. A request for a payment process is received and a client-side memory stack is populated with a series of functions corresponding to the requested payment process. The execution of each function is monitored to determine whether the series of functions and an order of execution of the functions from the client-side memory stack are the same as an expected series of functions and in an expected order corresponding to the payment process. The monitoring also determines whether the number and types of parameters called by the functions are the same as the expected number and types of parameters. The monitoring further determines whether the timing of the execution of the functions is the same as an expected timing. Remedial action is performed when the any of these factors is determined to be different than what is expected.

Abstract: There are provided systems and methods for named entity recognition in chat dialogues for customer relationship management systems. A service provider, such as an electronic transaction processor for digital transactions, may provide live chat service channels for assistance through live agents and chatbot services. When interacting with these channels, a user may engage in a chat dialogue with live agents. This may include lines of texts corresponding to the exchanged messages and may include named entities for particular types or categories of words that refer to a particular object or thing. To identify these named entities, a natural language processor may utilize machine learning and other engines for named entity recognition in customer relationship management systems to highlight the named entities in live service chats. Agents of the systems may view content that identify the named entities and interact with the named entities to view descriptions.

Abstract: Methods and systems are presented for providing a multi-party computation (MPC) framework for dynamically configuring, deploying, and utilizing an MPC system for performing distributed computations. Based on device attributes and network attributes associated with computer nodes that are available to be part of the MPC system, a configuration for the MPC system is determined. The configuration may specify a total number of computer nodes within the MPC system, a minimum number of computer nodes required to participate in performing a computation process, a key distribution mechanism, and a computation processing mechanism. Encryption keys are generated and distributed among the computer nodes based on the key distribution mechanism. Upon receiving a request for performing the computation, updated network attributes are obtained.

Abstract: There are provided systems and methods for automated adjustment of security alert components in networked computing systems. An entity, such as an electronic transaction processor for digital transactions, may utilize threat detection within a security information and event management system. The threat detection may implement one or more processes to tune security alerts automatically, which can be done prior to deployment. A security alert may be broken into modular components, which may be run progressively, in increasing sampling numbers, against a set of computing logs to identify hits. The hits are compared to an expected proportion for each modular component to determine whether the modular component is providing proper results. Further, threat detection may utilize a system to obtain justifications for potentially malicious behavior to eliminate false positives. This may be done automatically when detecting suspicious activities.

Abstract: Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.

Abstract: There are provided systems and methods for application data displayed through a selectable application icon. A computing device, such as a mobile smart phone, may include one or more processing application, such as a payment application to utilize an online payment provider. The application may include a desktop or folder icon, or other visual icon that may be presented within an interface of the device's operating system. Additionally, the application may include application data normally presented to the user of the device during operation of the application. The application or the device's operating system may select an informational element of the application information to present to the user. The informational element may then be presented on the application icon while the application is unopened or executing in the background of the operating system. Multiple informational elements may be cycled through on the application icon.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for transaction processing using a smart device. In one embodiment, a system and method are introduced which enable the use of a third-party provider in conjunction with the smart device. The third-party provider can enable transaction processing and offloading, eliminating the need for processing via an application server. In another embodiment, a dynamic machine-readable code is further introduced for providing the communication between the third-party provider and the smart device.

Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.

Abstract: Methods, systems, and computer program products are included for load balancing processing of a data stream that is communicated over a network. An exemplary method includes establishing a communication session over a network between a source endpoint device and a destination endpoint device. A network device in the network receives a data stream that is communicated to the destination endpoint device from the source endpoint device during the communication session. The network device provides data corresponding to the data stream to a processing device. The processing device identifies a portion of the data that is assigned to the processing device and performs operations to process the portion of the data. After performing the operations, the processing device communicates a response corresponding to the processing to the source endpoint device by way of the destination endpoint device.

Abstract: Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.

Abstract: Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.

Abstract: Methods and systems are presented for providing a framework for facilitating offline cryptocurrency transactions. A first application executed in a first secure enclave of a first device can register itself with a cryptocurrency computer network for initiating offline cryptocurrency transactions and reserve a denomination of cryptocurrency for the offline cryptocurrency transactions based on a token. The first application initiates an offline cryptocurrency transaction with a second application executed in a second enclave of a second device by transmitting a request comprising the token via a peer-to-peer connection. The second application verifies the request based on the token and attributes associated with the first application and the first secure enclave. Upon accepting the request, the second application stores the token in the second secure enclave.

Abstract: There are provided systems and methods for configuration-driven efficient transformation of formats and object structures for data specifications in computing services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. Different services may process data in different data formats and structures. However, transformation of data between different services, such as a gateway service that may receive data processing requests and/or data objects and downstream services that may process such requests and objects, may take significant time and resources. A configuration-driven data transformation platform may intelligently create code for and select from transformers that may be used for data transformations.

Abstract: Methods and systems are presented for providing a machine learning model framework configured to perform complex data classifications. Upon receiving a request for classifying data, the data is recursively assigned to one or more clusters. During each iteration of clustering assignment, a set of clusters is selected based on a previously assigned cluster for the data, and the data is then assigned to a particular cluster from the selected set of clusters. The machine learning model framework also includes a plurality of machine learning models configured to perform simple data classifications. A particular machine learning model is selected from the plurality of machine learning model based on the one or more clusters to which the document is assigned. The particular machine learning model is then used to classify the document.

Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.