Abstract: The system uses the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.

Abstract: Disclosed is an improved computer architecture for generating an electronic data layout by having a user input a portion of dataset and obtaining the remaining portion of the format data from a data storage that stores reusable data for various data layouts. A master data object is configured to store “request-agnostic data,” which is typically that portion of dataset that does not differ, or is common, between various data layouts. The data that differs between various data layouts, such as the data that is specific to a form, may be considered as “request-specific data.” When a data layout generation request is received, the user may be prompted to input request-specific data, but not the request-agnostic data. The system automatically obtains the request-agnostic data from the master data object, and integrates the request-agnostic data with the request-specific data to generate the form.

Abstract: The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.

Abstract: The present application relates to embodiments for detecting firewall drift. In some embodiments, a first set of firewall rules of a first firewall for a first instance of a distributed application, a second set of firewall rules of a second firewall for a second instance of the distributed application, and a mapping of IP addresses to identifiers of services from amongst a first set of services of the first instance and a second set of services of the second instance may be obtained. First connectivity data and second connectivity data may be generated indicating, for each of IP address associated with the first and second set of firewall rules, a respective port number over which communications between a respective IP address are transmitted, and generating comparison data indicating whether firewall drift is detected based on a comparison of the first connectivity data and the second connectivity data.

Abstract: A central service provider manages a blockchain network that writes the cryptographic hash of each executed transaction in a block to the blockchain network. For each executed transaction, the central service provider generates and transmits a transaction receipt such that a party can verify that the transaction was appropriately executed. Additionally, a party can check that the party's records are correct by providing transaction data describing details of transactions recorded in the party's records to the central service provider. The central service provider verifies the party's records by comparing the transaction data in the party's records to the blocks of transaction records in the blockchain network. In some scenarios, the central service provider may identify or receive an identification of a discrepancy arising from one or more transactions. The central service provider can reconcile the identified discrepancy.

Abstract: In certain embodiments, multi-modal-based generation of settlement instructions may be facilitated. In some embodiments, a portfolio of a live environment may be emulated in a projected environment. A target portfolio may be generated in the projected environment based on the emulated portfolio. Partial synchronization between the target portfolio of the projected environment and the portfolio of the live environment may be performed such that a first subset of changes to the portfolio of the live environment are reflected in the target portfolio of the projected environment. Subsequent to the partial synchronization, the target portfolio of the projected environment may be updated such that the update of the target portfolio accounts for the first subset of changes. Subsequent to the update of the target portfolio, settlement instructions may be generated based on differences between the target portfolio of the projected environment and the portfolio of the live environment.

Abstract: In certain embodiments, resolved exceptions information regarding resolved exceptions may be obtained. The resolved exceptions information may indicate the resolved exceptions and, for each resolved exception of the resolved exceptions, a set of attributes of a transaction for which the resolved exception was triggered. The resolved exceptions information may be provided as input to a prediction model to obtain multiple decision trees via the prediction model. Each decision tree of the multiple decision trees may comprise nodes and conditional branches, each node of the nodes of the decision tree indicating a probability of a dividend-related classification for a transaction that corresponds to the node. A decision tree may be obtained from the multiple decision trees.

Abstract: Provided is a system and method for enabling of access to a computer resource by a computer system comprising: providing to a user an interface configured to receive a request for access to a computer resource; determining if the user is permitted to access the computer resource based on a user profile; providing a user verification interface configured to receive user identity verification information; determining if the user identity verification information is valid in response to a reply to the request for user identify verification information; and in response to determining that the user is permitted access to the computer resource and that the user verification information is valid: updating a security policy to reflect that the user is permitted to access the computer resource, and providing access to the computer resource for a limited time duration.

Abstract: The system uses the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.

Abstract: Provided is a method including obtaining a plurality of time series data sets and a synthetic time series data set that is associated with a first machine learning label such that the first machine learning label is associated with a first time series data set of the plurality of time series data sets. The method includes inputting the synthetic time series data set, the first machine learning label, a second machine learning label that is associated with a second time series data set of the plurality of time series data sets, and a series of generated values in a time series simulation machine learning model that includes a trained generative adversarial network. The method includes running a generator neural network included in the trained generative adversarial network with the inputs and then generating a synthetic second time series data set for the second time series data set.

Abstract: A system stores transaction data in a ring chain architecture. A ring chain comprises blocks of data stored as a length-limited block chain in a ring buffer configuration. A block of transactions is stored on a ring chain until enough new blocks are added to overwrite the ring buffer with new blocks. The system stores multiple ring chains that update at varying frequencies. A new block on a lower frequency ring chain stores an aggregation of data from the blocks that were added to a higher frequency ring chain in the time since the previous addition of a block to the lower frequency ring chain. Thus, a system of ring chains stores progressively summarized state transition data over progressively longer time intervals while maintaining immutability of the record and reducing storage requirements.

Abstract: Provided is a failover automation system and method comprising: obtaining, by a processor, a process inventory for a failover of an application from a first datacenter to a second data center; generating, by the processor, a data model for the failover based on the process inventor; generating, by the processor, a workflow for the failover based on the data model; assembling, by the processor, a set of one or more virtual engineers to perform the failover for the application based on the workflow; and performing, by the processor, the failover for the application with the set of one or more virtual engineers based on the workflow.

Abstract: Provided is an architecture drift detection system and method including: obtaining a first set of architecture design metrics associated with a first application; obtaining first set of data metrics associated with a first instance of the first application that is installed at a first server computing system; obtaining a second set of data metrics associated with a second instance of the first application that is installed at a second server computing system; determining, using the first set of data metrics and the second set of data metrics, that at least one of the first instance, the first server computing system, the second instance, or the second server computing system deviates from one or more architecture design metrics from the first set of architecture design metrics associated with the first application; and providing a deviation notification indicating a deviation from the one or more architecture design metrics.

Abstract: Methods and systems are described for implementing automated controls assessment through an application programming interface (“API”) driven software development kit. For example, the system may receive a response from an API-based agent to an automated controls assessment audit. The system may process the response, using a library of reusable features for controls assessment audits for a plurality of computer domains, to generate a result of the automated controls assessment audit. The system may then generate an outcome of the first automated controls assessment audit.

Abstract: Methods and systems are described for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads. The methods and systems may include determining an initial allocation of the plurality of processing requests to the plurality of available domains that has a lowest initial sum excess processing load. The methods and systems may then retrieve an updated estimated processing load for at least one of the plurality of processing requests and determine a secondary allocation of the plurality of processing requests to the plurality of available domains.

Abstract: Methods and system are described for optimizing data processing in cloud-based, machine learning environments. For example, through the use of a machine learning model utilizing a self organizing map and/or the use of specific processing nodes in a computer system to perform specific tasks the methods and system may more efficiently distribute tasks through a cloud computing environment and increase overall processing speeds despite increasing amounts of data. The methods and system described herein are particularly related to collateral allocation computer systems that automate the management of numerous collateral assets. For example, as the amount of collateral assets and the complexity of given transactions grow, typical allocation systems face frequent processing delays related to collateral allocations (e.g., allocations of collateral associated with Tri-Party Repos).

Abstract: A system for processing performance prediction decisions includes one or more processors configured to execute one or more program modules. The modules are configured to receive, via the one or more processors, a prediction for an account at a prediction timestamp. The modules are also configured to identify, via the one or more processors, a prediction rule using attributes from the prediction. Responsive to the prediction rule having a network trigger associated therewith, the modules are configured to retrieve, via the one or more processors, a network trigger time associated with the network trigger, compare, via the one or more processors, the prediction timestamp to the network trigger time, and apply, via the one or more processors, a prediction decision based on the comparison of the prediction timestamp and the network trigger time. Applying the prediction decision includes determining a confidence level that a communication associated with the prediction will occur by a given time.

Abstract: A central service provider manages a blockchain network that writes the cryptographic hash of each executed transaction in a block to the blockchain network. For each executed transaction, the central service provider generates and transmits a transaction receipt such that a party can verify that the transaction was appropriately executed. Additionally, a party can check that the party's records are correct by providing transaction data describing details of transactions recorded in the party's records to the central service provider. The central service provider verifies the party's records by comparing the transaction data in the party's records to the blocks of transaction records in the blockchain network. In some scenarios, the central service provider may identify or receive an identification of a discrepancy arising from one or more transactions. The central service provider can reconcile the identified discrepancy.

Abstract: A method for real-time data processing is described. The method being implemented on a computer system having one or more physical processors programmed with computer program instructions which, when executed, perform the method. The method comprising allocating a real-time dataset associated with a real-time data interaction to a node in a chain of nodes, wherein each node is representative of a user in the real-time data interaction; setting a node status of the node for the real-time dataset to pending; and independently of (i) a node status of the one or more upstream nodes and (ii) a node status of the one or more downstream nodes: periodically determining, by the computer system, an availability status of the node; and in response to the availability status satisfying the criterion, setting the node status for the real-time dataset as settled.

Abstract: The methods and systems relate to generating hierarchical data structures based on crowdsourced data featuring non-homogenous metadata. For example, the methods and systems address challenges with conventional crowdsourcing platforms, which fail to provide a mechanism for identifying contributors that would be best fit for a task, particularly when there is no standardized criteria for that task. Additionally, the methods and systems negate the need for peer-vetting and manual review of contributions thus significantly limiting bottlenecks in the development process. The methods and systems provide these advantages through the use of a novel data structure generated based on machine learning.