Abstract: The disclosed embodiments include computer-implemented processes that predict a credit score for a customer in real-time based on an application of a trained machine-learning or artificial-intelligence process to encrypted event data at a third-party computing cluster. For example, an apparatus may transmit encrypted event data to a third-party computing system. The third-party computing system may apply a trained artificial intelligence process to encrypted feature data that includes the encrypted event data, and the apparatus may receive, from the third-party computing system, encrypted output data representative of a predicted credit score during at least one temporal interval. The apparatus may decrypt the encrypted output data using a homomorphic decryption key, and transmit a notification that includes the decrypted output data to a device. An application program executed at the device may present a graphical representation of at least a portion of the decrypted output data within a digital interface.

Abstract: The disclosed embodiments include computer-implemented processes that predict a credit score for a customer in real-time based on an application of a trained machine-learning or artificial-intelligence process to encrypted event data at a third-party computing cluster. For example, an apparatus may transmit encrypted event data to a third-party computing system. The third-party computing system may apply a trained artificial intelligence process to encrypted feature data that includes the encrypted event data, and the apparatus may receive, from the third-party computing system, encrypted output data representative of a predicted credit score during at least one temporal interval. The apparatus may decrypt the encrypted output data using a homomorphic decryption key, and transmit a notification that includes the decrypted output data to a device. An application program executed at the device may present a graphical representation of at least a portion of the decrypted output data within a digital interface.

Abstract: A processor-implemented method is disclosed. The method includes: obtaining sensor readings from a plurality of sensors associated with a plurality of homes; receiving, from a computing device associated with a first one of the plurality of homes, a request for a live home score; determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes.

Abstract: A processor-implemented method is disclosed. The method includes: obtaining sensor readings from sensors associated with a first home and a plurality of second homes; determining a set of prioritized maintenance items for the first home based on comparing sensor readings from sensors associated with the plurality of second homes to identify and rank maintenance items; detecting one or more maintenance events associated with one or more of the set of the prioritized maintenance items for the first home; and outputting a live home score for the first home based on detecting the one or more maintenance events.

Abstract: A processor-implemented method is disclosed. The method includes: obtaining sensor readings from sensors associated with a first home and a plurality of second homes; determining a set of prioritized maintenance items for the first home based on comparing sensor readings from sensors associated with the plurality of second homes to identify and rank maintenance items; detecting one or more maintenance events associated with one or more of the set of the prioritized maintenance items for the first home; and outputting a live home score for the first home based on detecting the one or more maintenance events.

Abstract: The disclosed embodiments include computer-implemented processes that predict a credit score for a customer in real-time based on an application of a trained machine-learning or artificial-intelligence process to encrypted event data at a third-party computing cluster. For example, an apparatus may transmit encrypted event data to a third-party computing system. The third-party computing system may apply a trained artificial intelligence process to encrypted feature data that includes the encrypted event data, and the apparatus may receive, from the third-party computing system, encrypted output data representative of a predicted credit score during at least one temporal interval. The apparatus may decrypt the encrypted output data using a homomorphic decryption key, and transmit a notification that includes the decrypted output data to a device. An application program executed at the device may present a graphical representation of at least a portion of the decrypted output data within a digital interface.

Abstract: A processor-implemented method is disclosed. The method includes: obtaining sensor readings from a plurality of sensors associated with a plurality of homes; receiving, from a computing device associated with a first one of the plurality of homes, a request for a live home score; determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes.

Abstract: A processor-implemented method is disclosed. The method includes: obtaining sensor readings from a plurality of sensors associated with a plurality of homes; receiving, from a computing device associated with a first one of the plurality of homes, a request for a live home score; determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes.

Abstract: The present disclosure involves, systems, software, and computer-implemented methods for determining a location for a curbside branch. One example system comprises a memory storing instructions; at least one hardware processor interoperably coupled with the memory, wherein the instructions instruct the at least one hardware processor to: obtain historical and current exchange data of users associated with a particular location; obtain external data about the particular location; enrich the historical and current exchange data with the external data; identify one or more analysis rules to apply to the enriched data to determine an estimated demand for services associated with deployment of a mobile site at the particular location; compare estimated demand to a predetermined threshold; and in response to a determination that the estimated demand exceeds the predetermined threshold, identify the particular location for mobile site deployment.