Abstract: The systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. For example, the model may predict how a rate of change in time-series data may be altered throughout the first time period based on the predicted event. However, as noted above, correctly predicting the occurrence of outlier events (e.g., the predicted event), and in particular characteristics about the outlier events (e.g., when an outlier may occur, what may be a source of the outlier, what rate of change the outlier may cause, etc.), in data-sparse environments and based on time-series data presents a technical challenge.

Abstract: The systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. For example, the model may predict how a rate of change in time-series data may be altered throughout the first time period based on the predicted event. In addition, the system may use a machine learning model to classify a user, based on current state characteristics, into a class of users and identify peers of the user. Based on those peers, the system may identify items for the user and generate a predetermined output variable.

Abstract: Presented herein are systems and methods for generating suspicious activity reports using large language models. A system may include one or more processors that obtain event data associated with an event from a client device and from one or more databases, apply a prompt generator on the event data to generate a large language model (LLM) prompt, and generate a machine-readable suspicious activity (SAR) report in accordance with an LLM prompt. The one or more processors may also apply the prompt generator on the event data based on determining that a fraud risk score associated with the event satisfies a reporting threshold score. Computer program products are also presented.

Abstract: Systems and methods for proportional maintenance of complex computing systems. By using proportional maintenance (e.g., recommending/allocating resources based on current usage in the computing system), the systems and methods may scale current resources (e.g., hardware and/or software components) based on how those resources are currently utilized.

Abstract: Presented herein are systems and methods for aggregating data from disparate sources to output information. A computing system may transform a first plurality of datasets of a plurality of data sources by converting a first format of the corresponding data source for each of the first plurality of datasets to generate a second plurality of datasets in a second format of the computing system. The computing system may identify, from the second plurality of datasets, a subset of datasets using a feature selected for evaluation of a utility of the feature. The computing system may apply a machine learning model configured for the selected feature to the subset of datasets to generate an output that measures a likelihood of usefulness. The computing system may cause a visualization of the output for the feature to be displayed for presentation on a dashboard interface based on a template configured for the feature.

Abstract: The systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. For example, the model may predict how a rate of change in time-series data may be altered throughout the first time period based on the predicted event. However, as noted above, correctly predicting the occurrence of outlier events (e.g., the predicted event), and in particular characteristics about the outlier events (e.g., when an outlier may occur, what may be a source of the outlier, what rate of change the outlier may cause, etc.), in data-sparse environments and based on time-series data presents a technical challenge.