Abstract: Exemplary embodiments may provide keys for unlocking access to a location, like a user room, a secure location, a door at an employee location, a trunk, a closet or other locked location or item, on a payment card. Examples of a payment card include but are not limited to a credit card, a debit card, a smart card, an employee identification card, etc. A secure token that acts as digital key may be uploaded to the payment card. The payment card may then be put in close proximity of a wireless reader at the lock. The wireless reader obtains the secure token and extracts the contents. If the contents are proper, the lock is unlocked. Otherwise, the lock remains locked.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: Disclosed embodiments may include a system that may receive an interaction message associated with an interaction a user has with an application or website, the interaction message may include an error message or a repeated action message. The system may identify, using a first machine learning model, one or more issues associated with the interaction message, retrieve one or more troubleshooting steps mapped to the one or more issues, and generate a first message comprising the one or more troubleshooting steps and a feedback request on an effectiveness of the one or more troubleshooting steps. The system may transmit the first message to the user, receive feedback from the user in response to the feedback request, and determine whether the feedback is negative. When the feedback is negative, the system may transmit a second message to a representative requesting the representative call the user.

Abstract: In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Abstract: Disclosed embodiments may include a system that may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Aspects described herein may provide modification of recommendations from a recommendation engine for a product or a device. The recommendation engine may provide initial search results for a particular type of device. The initial search results may be modified to ensure inclusion of devices that include features that the user considers important. Features that the user considers important may be determined based on observing the user's interaction with another device of the same type. By observing the user's interaction with the other device over a period of time, features that the user commonly uses and features that the user sparingly uses may be determined. The initial search results may then be modified to remove devices that do not include the features frequently used by the user and therefore considered important to the user.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

Abstract: A method and system include receiving, by a processor of a server, from a computing device associated with a user, real-time user activity data identifying at least one activity performed on the computing device. User-inputted data elements from a plurality of elements of a graphical user interface displayed on the computing device are received, which identify user-specific data attributes. Potential user-specific knowledge information is identified from databases based on at least one user-specific data attribute. User-specific challenge questions based on the potential user-specific knowledge information are generated and displayed on the user's computing device. Answers to the user-specific challenge questions by the user are received. An answer score based on correct answers and a behavioral score based the real-time user activity data of the user are determined. The processor determines whether the user is or is not a fraudster based on the answer score and the behavioral score.

Abstract: Methods and systems are disclosed herein for secure communication between computing devices. A mobile device may communicate with an untrusted device to cause the untrusted device to send information (e.g., encrypted information that the untrusted device is unable to decrypt) to a server using an Internet connection of the untrusted device. The mobile device may have limited or no access to the Internet. To prevent potential security risks associated with using a public or untrusted device, the mobile device may encrypt information stored on the mobile device (e.g., stored in a mobile application associated with the server), send it to the untrusted device (e.g., by displaying a QR code to a camera of the untrusted device), and the untrusted device may send the information to the server via a network connection of the untrusted device.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: The present disclosure describes aggregating information from one or more gig economy platforms. A data aggregator may receive an indication that a user has worked for one or more gig economy platforms. The indication may include the user's login credentials. The data aggregator may then use the login credentials to retrieve user information from each of the one or more gig economy platforms. The server may aggregate and normalize the user information, such that the information may be presented to the user in a consumable format. Additionally, the user information may be used to render decisions and/or make determinations about the user based on the user's employment records associated with the one or more gig economy platforms.

Abstract: Aspects described herein may provide modification of recommendations from a recommendation engine for a product or a device. The recommendation engine may provide initial search results for a particular type of device. The initial search results may be modified to ensure inclusion of devices that include features that the user considers important. Features that the user considers important may be determined based on observing the user's interaction with another device of the same type. By observing the user's interaction with the other device over a period of time, features that the user commonly uses and features that the user sparingly uses may be determined. The initial search results may then be modified to remove devices that do not include the features frequently used by the user and therefore considered important to the user.

Abstract: Aspects described herein may allow for a payment card having a first surface and an opposed second surface. A plurality of surface irregularities is formed on the first surface, with the plurality of surface irregularities defining alphanumeric characters. A phosphorescent coating is formed on at least a portion of the surface irregularities.

Abstract: Aspects described herein may provide modification of recommendations from a recommendation engine for a product or a device. The recommendation engine may provide initial search results for a particular type of device. The initial search results may be modified to ensure inclusion of devices that include features that the user considers important. Features that the user considers important may be determined based on observing the user's interaction with another device of the same type. By observing the user's interaction with the other device over a period of time, features that the user commonly uses and features that the user sparingly uses may be determined. The initial search results may then be modified to remove devices that do not include the features frequently used by the user and therefore considered important to the user.

Abstract: A system for dynamically routing customer calls. For example, the system may receive user interaction data associated with a first user using a first user device. The system may also receive a phone call from a user using a first phone number. The system may also identify the user via the first phone number. The system may determine, using a first machine learning model, whether the first user has a first emotion type based on the user interaction data. When the first user does not have the first emotion type, the system may route the first user to any call center representative. When the first user has the first emotion type, the system may route the first user to a first call center representative among one or more first call center representatives.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.