Abstract: A system for identifying false positives about a user and closing resulting alerts before the alert consumes system resources may use machine learning techniques including clustering and multi-labeling classification to effectively categorize prior text-based notes. The categorized notes may be used to identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis may be used to condense large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features for improved alert triaging.

Abstract: Aspects of the disclosure relate to smart sampling of noisy labels using artificial intelligence. A computing platform may receive a dataset of primarily unlabeled data points. The computing platform may apply undersampling to the unlabeled data points to reduce imbalance. The computing platform may assign a candidate label to each unlabeled data point in the dataset without a human manually labeling the unlabeled data points. The computing platform may compute a heuristic score for each data point and rank the data points based on the heuristic score. The computing platform may subsample the dataset by comparing the heuristic score for each data point against more than one threshold and applying a k-Nearest Neighbors (k-NN) algorithm, with two different k values, to identify untrustworthy labels. The computing platform may provide or transmit a trustworthy resulting dataset to a machine learning (ML) model.

Abstract: Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and parse, using a machine learning model, the text-based instruction to generate a structured resource distribution including predicted distribution elements. The present invention may be configured to generate, based on the structured resource distribution, a resource distribution. In some embodiments, the text-based instruction may include an email message, an SMS message, recorded speech converted to text, text input to a chat function, text recognized in an image, and/or the like.

Abstract: Systems, computer program products, and methods are described herein for machine learning-driven data record retrieval via stochastic expansion data querying. Data records and a query statement are received. Using a weighting model, a data vector for each of the data records and a query vector for the query statement are generated. A first similarity score for each data vector is determined. Rankings for the data records are generated. Based on the rankings, a grouping of data records is selected. Test vectors centered from the query vector are then generated stochastically. A second similarity score for each of the test vectors is determined. A subsequent query vector is determined based on a highest second similarity score. If metrics do not satisfy a predetermined stopping criteria, subsequent test vectors are generated stochastically. Ranked data records are then provided to a large language model.

Abstract: Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and parse, using a machine learning model, the text-based instruction to generate a structured resource distribution including predicted distribution elements. The present invention may be configured to generate, based on the structured resource distribution, a resource distribution. In some embodiments, the text-based instruction may include an email message, an SMS message, recorded speech converted to text, text input to a chat function, text recognized in an image, and/or the like.

Abstract: Aspects of the disclosure relate to smart sampling of noisy labels using artificial intelligence. A computing platform may receive a dataset of primarily unlabeled data points. The computing platform may apply undersampling to the unlabeled data points to reduce imbalance. The computing platform may assign a candidate label to each unlabeled data point in the dataset without a human manually labeling the unlabeled data points. The computing platform may compute a heuristic score for each data point and rank the data points based on the heuristic score. The computing platform may subsample the dataset by comparing the heuristic score for each data point against more than one threshold and applying a k-Nearest Neighbors (k-NN) algorithm, with two different k values, to identify untrustworthy labels. The computing platform may provide or transmit a trustworthy resulting dataset to a machine learning (ML) model.

Abstract: The system identifies false positives about a user and closes resulting alerts before the alert wastefully consume system resources. Machine learning techniques including clustering and multi-labeling classification are used to effectively categorize prior text-based notes to efficiently identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis provide a means for condensing large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures inefficiently/ineffectively organized past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features that directly improve model performance for alert triaging as a whole.

Abstract: The system identifies false positives about a user and closes resulting alerts before the alert wastefully consume system resources. Machine learning techniques including clustering and multi-labeling classification are used to effectively categorize prior text-based notes to efficiently identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis provide a means for condensing large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures inefficiently/ineffectively organized past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features that directly improve model performance for alert triaging as a whole.

Abstract: Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent. The present invention may be configured to determine, based on user data in a user information data structure, a user associated with the sender alias, determine, based on user data associated with the user in the user information data structure and based on predicted distribution elements from a machine learning model, actual distribution elements, and generate, based on the actual distribution elements, a resource distribution.

Abstract: Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and parse, using a machine learning model, the text-based instruction to generate a structured resource distribution including predicted distribution elements. The present invention may be configured to generate, based on the structured resource distribution, a resource distribution. In some embodiments, the text-based instruction may include an email message, an SMS message, recorded speech converted to text, text input to a chat function, text recognized in an image, and/or the like.

Abstract: Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent. The present invention may be configured to determine, based on user data in a user information data structure, a user associated with the sender alias, determine, based on user data associated with the user in the user information data structure and based on predicted distribution elements from a machine learning model, actual distribution elements, and generate, based on the actual distribution elements, a resource distribution.