Abstract: Methods, apparatus, and processor-readable storage media for automated request processing using an ensemble machine learning framework are provided herein. An example computer-implemented method includes aggregating interaction data associated with a request; computing a weighted score for the request, wherein the weighted score comprises a first component that is based at least in part on a comparison of the aggregated interaction data to a set of keywords and a second component corresponding to a sentiment predicted by a first machine learning model for at least a portion of the aggregated interaction data; using a second machine learning model to determine whether the request is anomalous based at least in part on the weighted score; and in response to determining that the request is anomalous, initiating one or more automated actions for the request.

Abstract: Techniques described herein relate to a method for generating address recommendations. The method includes obtaining, by a recommendation system, an address recommendation request associated with an address, wherein the address is associated with a user; in response to obtaining the address recommendation request: generating a context vector associated with the address; generating an address recommendation based on the context vector using a recommendation model; obtaining user feedback associated with the address recommendation; generating a reward based on the user feedback; and updating the recommendation model based on the context vector, the address recommendation and the reward.

Abstract: Techniques described herein relate to a method for identifying anomalies in compensation data. The method includes identifying a compensation data anomaly detection event; in response to identifying the compensation data anomaly detection event: obtaining compensation data associated with the compensation data anomaly detection event; performing preprocessing on the compensation data to generate updated compensation data; performing feature grouping on the updated compensation data to generate grouped compensation data; performing change discovery using the grouped compensation data to identify potential anomalies; generating a comparative anomaly prediction using the potential anomalies; and performing anomaly remediation actions based on the comparative anomaly prediction.

Abstract: Methods, apparatus, and processor-readable storage media for automated address data determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes extracting address information from one or more documents using at least one artificial intelligence-based address extraction model; parsing, into one or more address components, at least a portion of the extracted address information using at least one artificial intelligence-based parsing model; comparing at least a portion of the one or more parsed address components to stored address information using at least one artificial intelligence-based scoring model; and performing one or more automated actions based at least in part on results from the comparing of at least a portion of the one or more parsed address components to stored address information.

Abstract: Methods, apparatus, and processor-readable storage media for automated communication data summarization using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying one or more words relevant to one or more predefined tasks by processing input communication data using a first set of one or more artificial intelligence techniques; extracting, based at least in part on the identifying of the one or more words, multiple portions of the input communication data into individual sub-portions of the input communication data; generating at least one summarization of the input communication data based at least in part on processing the sub-portions of the input communication data using a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one summarization of the input communication data.