Abstract: Methods and systems for estimating a vehicle status using interaction data are disclosed. An exemplary method can include obtaining, via at least one processor, information associated with a vehicle; obtaining, via the at least one processor, a set of interaction data of an individual associated with the vehicle; and accessing, via the at least one processor and on a memory, a framework indicative of an association between interaction data and vehicles status. An exemplary method may further include generating, via the at least one processor and by applying the framework to the set of interaction data and the information associated with the vehicle, an estimate of the vehicle status; generating, via the at least one processor and based on the estimate of the vehicle status, at least one evaluation for the vehicle; and outputting the at least one evaluation.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a permission indicator identifying a permission by the first user to detect calls being received by the computing device; receiving an indication of a particular call being received from a second user at a particular phone number; detecting a triggering condition to verify when the particular phone number is associated with the particular entity; instructing the computing device to execute a computer routine to verify when the particular phone number is associated with the particular entity; receiving an indication from the computer routine when the particular phone number is not associated with the particular entity; instructing the computing device to perform at least one security action; and instructing to notify a second computing device associated with the particular entity with information pertaining to the particular call.

Abstract: Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.

Abstract: Systems and methods of provisioning post-interaction values are disclosed.

Abstract: Disclosed embodiments pertain to provisioning a virtual payment card for an authorized user in a browser. A primary cardholder can request the addition of an individual as an authorized user on the primary cardholder's account. Further information regarding the individual can be solicited and utilized to compute a risk score associated with adding the individual as an authorized user. Further processing of the request, including whether to terminate or continue, can be based on comparing the risk score to a predetermined threshold. If processing is continued, a virtual payment card linked to the primary cardholder account is generated. Next, the virtual card can be added as a payment method to a browser profile of the individual on a network-accessible computing resource. Subsequently, the authorized user can access the virtual card to make online shopping payments with a web browser personalized with the browser profile.

Abstract: Systems and methods for detecting and repairing loss of a primary digital communication channel may include a server and a user device. The server may be configured to send a push notification to an application of the user device over a network, receive, in response to the sending of the push notification, push notification status data, apply a predictive model to determine whether the primary digital communication channel has failed based on the push notification and the push notification status data; and transmit, upon a determination that the primary digital communication channel has failed, a communication to the user over one or more alternative digital communication channels.

Abstract: Embodiments described herein reduce resource insufficiency of a resource source despite inconsistent resource accumulation at the resource source. For example, a request frequency may be determined to define times at which the resource source is predicted to be sufficient despite the inconsistent accumulation or influx. In one use case, with respect to a distributed computing environment having computing resource source(s)/pool(s), a requesting system may identify a machine learning model trained to generate predictions for a resource source at which inconsistent resource accumulation occurs. The system may obtain accumulation data that describes accumulation events at which resources were made available at the resource source.

Abstract: Disclosed aspects and embodiments pertain to customer distress assistance. A call can be detected to a callee from a caller. The call can be analyzed and determined to be a fraud or spam risk. In response, callee monitoring is triggered. The monitoring can capture biometrics or speech of the callee. Distress in the callee can be determined through analysis of the biometrics or speech and comparison to reference biometrics or speech of the callee. An individual associated with the callee can be selected based on determining the distress in the customer. The individual can be contacted through a notification or a call to assist the callee in distress.

Abstract: Disclosed embodiments pertain to provisioning a virtual payment card for an authorized user in a browser. A primary cardholder can request the addition of an individual as an authorized user on the primary cardholder's account. Further information regarding the individual can be solicited and utilized to compute a risk score associated with adding the individual as an authorized user. Further processing of the request, including whether to terminate or continue, can be based on comparing the risk score to a predetermined threshold. If processing is continued, a virtual payment card linked to the primary cardholder account is generated. Next, the virtual card can be added as a payment method to a browser profile of the individual on a network-accessible computing resource. Subsequently, the authorized user can access the virtual card to make online shopping payments with a web browser personalized with the browser profile.

Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.

Abstract: A computer-implemented method for authenticating a source of a telephone call to a recipient of the telephone call may include receiving, prior to receiving the telephone call, a request to input first authentication information. The first authentication information may include a passcode or a selection of a security question. The method may include receiving input of the first authentication information and sending, by the user device, the first authentication information to a computing device associated with the source of the telephone call. The method may include receiving the telephone call from the source and receiving input from the recipient of the telephone call to answer the telephone call. The method may include receiving second authentication information from the computing device, where the second authentication information may include the passcode or an answer to the security question. The method may include outputting the second authentication information.

Abstract: Systems and methods for confirming and/or updating account information are disclosed. The systems and methods may calculate relocation scores based on transaction data and device location data. The relocation scores may be based on locations of the transactions and/or locations of a user device beyond a registered customer address. A relocation score can be calculated based on a quantity of transactions having transaction locations beyond a first predetermined distance from the customer address as determined by transaction location identifiers. A relocation score can be calculated based on an elapsed duration of time since a customer transaction was completed within the first predetermined distance from the customer address.

Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.

Abstract: Systems and methods for confirming and/or updating account information are disclosed. The systems and methods may calculate relocation scores based on transaction data and device location data. The relocation scores may be based on locations of the transactions and/or locations of a user device beyond a registered customer address. A relocation score can be calculated based on a quantity of transactions having transaction locations beyond a first predetermined distance from the customer address as determined by transaction location identifiers. A relocation score can be calculated based on an elapsed duration of time since a customer transaction was completed within the first predetermined distance from the customer address.

Abstract: Systems and methods of provisioning post-interaction values are disclosed.

Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.