Abstract: One embodiment of the present disclosure is a method for automatically acquiring and assessing travel data. The method includes acquiring travel data from a plurality of content sources using a plurality of artificial intelligence (AI) agents, generating, using the plurality of AI agents, a plurality of output travel data items from the acquired travel data, generating a plurality of confidence scores for the plurality of output travel data items, each of the plurality of confidence scores for a different respective output travel data item, and each of the plurality of confidence scores generated using a source credibility associated with a reliability of the content source from which the travel data was acquired for the output travel data item, and utilizing the plurality of confidence scores to determine an action to perform with respect to one or more of the plurality of output travel data items.

Abstract: One embodiment of the present disclosure is a method for automatically acquiring and assessing travel data. The method includes acquiring travel data from a plurality of content sources using a plurality of AI agents, generating, using the AI agents, a plurality of output travel data items from the acquired travel data and a plurality of confidence scores for the output travel data items, generating a plurality of final confidence scores for the plurality of output travel data items, the plurality of final confidence scores generated based the confidence scores, source credibility scores and AI agent credibility scores, automatically determining an action to perform on one or more of the plurality of output travel data items using the final confidence scores of the one or more output travel data items, and automatically performing the action.

Abstract: One embodiment of the present disclosure is a method for automatically acquiring and assessing travel data. The method includes acquiring travel data from a plurality of content sources using a plurality of AI agents, generating, using the AI agents, a plurality of output travel data items from the acquired travel data and a plurality of confidence scores for the output travel data items, generating a plurality of final confidence scores for the plurality of output travel data items, the plurality of final confidence scores generated based the confidence scores, source credibility scores and AI agent credibility scores, automatically determining an action to perform on one or more of the plurality of output travel data items using the final confidence scores of the one or more output travel data items, and automatically performing the action.

Abstract: A computing system for automated fraud risk reduction for travel-related transactions, the computing system including at least one processing circuit including at least one processor and at least one memory, the at least one memory storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive data corresponding to a first travel-related transaction, process, using a first machine learning model, the data to automatically generate an output data set comprising a plurality of characteristics relating to the first travel-related transaction, the first machine learning model configured to generate the output data set by identifying the plurality of characteristics to include responsive to determining the plurality of characteristics are potentially relevant to a determination of whether the first travel-related transaction is fraudulent, and provide the generated output data set for use in analyzing whether the first travel-related transaction is frau