Abstract: A method and system for generating predictive data related to institutional risks. An application programming interface (API) request for data includes multiple fields indicating a data type parameter, a dataset to be accessed, and operations to be performed on the dataset to generate first model output data. The first model output data is generated using a trained document data machine learning model. The operations indicated in the fields of the API request are performed. A data format associated with the requestor device is determined. A data stream usable by the requestor device is generated by formatting the data stream into a particular structure usable by the requesting device, the generated data stream based on the first model output data and at least one criterion of the API request. The formatted data stream is sent to a trained statistical data machine learning model to generate predictive data related to institutional risks.

Abstract: A method for training and using a machine-learning based model to reduce and troubleshoot incidents in a system may include receiving first metadata regarding a previous modification, extracting a first feature from the received first metadata, receiving second metadata regarding a previous incident, extracting a second feature from the received second metadata, training the machine-learning based model to learn an association between the previous modification and the previous incident, based on the extracted first feature and the extracted second feature, and using the machine-learning based model to determine a risk level for a proposed modification to a system.

Abstract: Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.

Abstract: A method for training and using a machine-learning based model to reduce and troubleshoot incidents in a system may include receiving first metadata regarding a previous modification, extracting a first feature from the received first metadata, receiving second metadata regarding a previous incident, extracting a second feature from the received second metadata, training the machine-learning based model to learn an association between the previous modification and the previous incident, based on the extracted first feature and the extracted second feature, and using the machine-learning based model to determine a risk level for a proposed modification to a system.

Abstract: Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.

Abstract: A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.

Abstract: A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.

Abstract: Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language product response.

Abstract: Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language advertisement response.

Abstract: Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language market response.

Abstract: Systems and methods for developing temporal behavioral profiles are disclosed.

Abstract: Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.

Abstract: A method for training and using a machine-learning based model to reduce and troubleshoot incidents in a system may include receiving first metadata regarding a previous modification, extracting a first feature from the received first metadata, receiving second metadata regarding a previous incident, extracting a second feature from the received second metadata, training the machine-learning based model to learn an association between the previous modification and the previous incident, based on the extracted first feature and the extracted second feature, and using the machine-learning based model to determine a risk level for a proposed modification to a system.

Abstract: Systems and methods for activity risk management are disclosed. A system for activity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: accessing document data associated with at least one of a transaction or an individual; normalizing the document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model to the extracted model input data to score the document data, the machine learning model having been trained to generate a favorability output indicating a favorability of the transaction or individual; and generating analysis data based on the scored document data.

Abstract: A method for training and using a machine-learning based model to reduce and troubleshoot incidents in a system may include receiving first metadata regarding a previous modification, extracting a first feature from the received first metadata, receiving second metadata regarding a previous incident, extracting a second feature from the received second metadata, training the machine-learning based model to learn an association between the previous modification and the previous incident, based on the extracted first feature and the extracted second feature, and using the machine-learning based model to determine a risk level for a proposed modification to a system.

Abstract: Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.

Abstract: Systems, devices, methods, and computer readable media for providing regulatory insight analysis are disclosed. In one implementation, the disclosed system may receive input data from a plurality of sources. Consistent with disclosed embodiments, the system may normalize the received input data. Further, the system may analyze the normalized input data, the analyzing comprising using logic for generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules. The system may further be configured to store the output, continuously monitor the output as the output is stored, and generate one or more reports based on the stored output. Further, the system may receive, from a user and via a user interface, additional input data, a request to view the one or more generated reports, or a request for an additional output.

Abstract: Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.

Abstract: A computer-implemented method for transaction settlement prediction may include receiving data for a plurality of past financial trades, training a machine learning model using the data for the plurality of past financial trades, receiving one or more parameters for a subject financial trade among a plurality of recently executed financial trades, determining a likelihood that the subject financial trade will fail using the trained machine learning model, determining a most likely reason that the subject financial trade will fail using the trained machine learning model, and presenting the likelihood that the subject financial trade will fail and the most likely reason that the subject financial trade will fail to a user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences of multi-modal entity data using convolutional neural networks. One of the methods includes receiving an input sequence of multi-modal feature vectors characterizing an entity over a time window, wherein each multi-modal feature vector in the input sequence corresponds to a different time interval during the time window; processing the input sequence of multi-modal feature vectors using a convolutional neural network to generate a latent sequence that comprises a plurality of latent feature vectors; processing the latent sequence of latent feature vectors using an aggregation neural network to generate an aggregated feature vector; and processing the aggregated feature vector using an output neural network to generate a prediction that characterizes the entity after the time window.