Abstract: In some embodiments, after authentication of a user at a trusted device, a system may store a record with user or other support information and a code in a database. When support is requested by the user device, the code provided in response to the request (e.g., as an extension to a phone number or in a header field), and a system may use the code to retrieve the record from the database. Presence of the record in the database may indicate to the communication system that the user requesting support is already authenticated.

Abstract: Systems as described herein may include website data surfacing. A data surfacing system may provide user interaction information to a machine learning model as input. Based on output from the machine learning model, the system may determine a likelihood that the user would navigate away from a website. After determining that the likelihood exceeds a threshold value and before the user navigates away from the website, the system may determine a second product in an inventory list that is similar to the first product on the website that is out of stock using a second machine learning model. Accordingly, the system may cause display of the second product in an overlay window displayed in the website using a browser extension on a user device.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval. The system may, in response to determining a subset of the plurality of locations where the average density for the respective first queuing areas at the first time interval exceed the threshold average density, generate for display, on the user device, a recommendation for a location from the subset at which to access the first service at the first time interval.

Abstract: In some embodiments, after authentication of a user at a trusted device, a system may store a record with user or other support information and a code in a database. When support is requested by the user device, the code provided in response to the request (e.g., as an extension to a phone number or in a header field), and a system may use the code to retrieve the record from the database. Presence of the record in the database may indicate to the communication system that the user requesting support is already authenticated.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval. The system may, in response to determining a subset of the plurality of locations where the average density for the respective first queuing areas at the first time interval exceed the threshold average density, generate for display, on the user device, a recommendation for a location from the subset at which to access the first service at the first time interval.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval, wherein the respective first queuing areas correspond to respective portions at each of the plurality of locations reserved for queuing users for the first service. The system may determine whether the average density for the respective first queuing areas at each of the plurality of locations at the first time interval exceeds a threshold average density.

Abstract: Methods, systems, and apparatuses are described herein for protecting user accounts using machine learning models. A machine learning model may be trained to determine whether account activity indicates a risk to credit scores. Account data, associated with a first financial account, may processed to determine whether the first financial account is associated with at least one underage user. A transaction request, associated with the first financial account, may be received. A history of transactions conducted by the first financial account may be retrieved. The trained machine learning model may be provided, as input, the transaction request and the history of transactions. An indication of risk to a credit score associated with the at least one underage user may be received as output from the trained machine learning model. A limitation may be added to the first financial account based on the indication of risk.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval, wherein the respective first queuing areas correspond to respective portions at each of the plurality of locations reserved for queuing users for the first service. The system may determine whether the average density for the respective first queuing areas at each of the plurality of locations at the first time interval exceeds a threshold average density.

Abstract: Methods, systems, and apparatuses are described herein for protecting user accounts using machine learning models. A machine learning model may be trained to determine whether account activity indicates a risk to credit scores. Account data, associated with a first financial account, may processed to determine whether the first financial account is associated with at least one underage user. A transaction request, associated with the first financial account, may be received. A history of transactions conducted by the first financial account may be retrieved. The trained machine learning model may be provided, as input, the transaction request and the history of transactions. An indication of risk to a credit score associated with the at least one underage user may be received as output from the trained machine learning model. A limitation may be added to the first financial account based on the indication of risk.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval, wherein the respective first queuing areas correspond to respective portions at each of the plurality of locations reserved for queuing users for the first service. The system may determine whether the average density for the respective first queuing areas at each of the plurality of locations at the first time interval exceeds a threshold average density.

Abstract: Methods and systems are discussed for location-based guidance based on interior conditions at a plurality of locations. For example, the system may receive, from a user device of a user, a user request to access a first service. The system may retrieve a plurality of locations that provide the first service. The system may retrieve an average density for respective first queuing areas at each of the plurality of locations at a first time interval, wherein the respective first queuing areas correspond to respective portions at each of the plurality of locations reserved for queuing users for the first service. The system may determine whether the average density for the respective first queuing areas at each of the plurality of locations at the first time interval exceeds a threshold average density.