Abstract: Systems and method for linking a social media handle to a financial account and providing a payment handle are disclosed. The payment handle is a unique identifier that allows a social media user to enter the payment handle in a checkout page within the social media ecosystem to authorize payment for goods or services. An account provider system can create a first payment handle to associate with a first social media system and a second payment handle to associate with a second social media system. The payment handle can be generated according to a list of preferred goods, merchant lists, or maximum transaction amounts. Rather than entering a credit card number, expiration date, address, and other data into a conventional checkout page, the system can enable a user to checkout simply by entering the payment handle and hitting “submit” (or similar).

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems for securing transactions based on merchant trust score are disclosed. The system may receive information identifying a merchant from a user device and, in response, retrieve transaction data associated with the merchant and receive website data in response to receiving information identifying the merchant. The system may use a machine learning model to generate a merchant trust score for the merchant, and determine whether the merchant trust score is less than a predetermined threshold. The system may also generate or retrieve an alternative payment method and provide related information or a recommendation to the user device.

Abstract: The disclosed technology relates to Spend Limit Systems for gas-station refuel transactions. An exemplary system may receive vehicle information identifying a customer's vehicle and identify associated vehicle characteristics (e.g., gas tank size). The system may identify a known gas-station transaction geographic region for the customer. When the customer (or a fraudster) attempts to purchase gas, the system may receive a transaction request and, in response, set a single transaction limit sufficient to fill the gas tank size with gas and an associated spend limit. The system may also determine whether the merchant location falls within the known gas-station transaction geographic region. When it does, the system may approve the transaction request up to the spend limit. Otherwise, the system may perform one or more fraud prevention actions to authenticate the customer and either approve or deny the transaction request up to the spend limit and/or selectively perform fraud mitigation action(s).

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems for securing transactions based on merchant trust score are disclosed. The system may receive information identifying a merchant from a user device and, in response, retrieve transaction data associated with the merchant and receive website data in response to receiving information identifying the merchant. The system may use a machine learning model to generate a merchant trust score for the merchant, and determine whether the merchant trust score is less than a predetermined threshold. The system may also generate or retrieve an alternative payment method and provide related information or a recommendation to the user device.

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems for securing transactions based on merchant trust score are disclosed. The system may receive information identifying a merchant from a user device and, in response, retrieve transaction data associated with the merchant and receive website data in response to receiving information identifying the merchant. The system may use a machine learning model to generate a merchant trust score for the merchant, and determine whether the merchant trust score is less than a predetermined threshold. The system may also generate or retrieve an alternative payment method and provide related information or a recommendation to the user device.

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: The disclosed technology relates to Spend Limit Systems for gas-station refuel transactions. An exemplary system may receive vehicle information identifying a customer's vehicle and identify associated vehicle characteristics (e.g., gas tank size). The system may identify a known gas-station transaction geographic region for the customer. When the customer (or a fraudster) attempts to purchase gas, the system may receive a transaction request and, in response, set a single transaction limit sufficient to fill the gas tank size with gas and an associated spend limit. The system may also determine whether the merchant location falls within the known gas-station transaction geographic region. When it does, the system may approve the transaction request up to the spend limit. Otherwise, the system may perform one or more fraud prevention actions to authenticate the customer and either approve or deny the transaction request up to the spend limit and/or selectively perform fraud mitigation action(s).

Abstract: The disclosed technology relates to Spend Limit Systems for gas-station refuel transactions. An exemplary system may receive vehicle information identifying a customer's vehicle and identify associated vehicle characteristics (e.g., gas tank size). The system may identify a known gas-station transaction geographic region for the customer. When the customer (or a fraudster) attempts to purchase gas, the system may receive a transaction request and, in response, set a single transaction limit sufficient to fill the gas tank size with gas and an associated spend limit. The system may also determine whether the merchant location falls within the known gas-station transaction geographic region. When it does, the system may approve the transaction request up to the spend limit. Otherwise, the system may perform one or more fraud prevention actions to authenticate the customer and either approve or deny the transaction request up to the spend limit and/or selectively perform fraud mitigation action(s).

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems for securing transactions based on merchant trust score are disclosed. The system may receive information identifying a merchant from a user device and, in response, retrieve transaction data associated with the merchant and receive website data in response to receiving information identifying the merchant. The system may use a machine learning model to generate a merchant trust score for the merchant, and determine whether the merchant trust score is less than a predetermined threshold. The system may also generate or retrieve an alternative payment method and provide related information or a recommendation to the user device.

Abstract: The disclosed technology relates to Spend Limit Systems for gas-station refuel transactions. An exemplary system may receive vehicle information identifying a customer's vehicle and identify associated vehicle characteristics (e.g., gas tank size). The system may identify a known gas-station transaction geographic region for the customer. When the customer (or a fraudster) attempts to purchase gas, the system may receive a transaction request and, in response, set a single transaction limit sufficient to fill the gas tank size with gas and an associated spend limit. The system may also determine whether the merchant location falls within the known gas-station transaction geographic region. When it does, the system may approve the transaction request up to the spend limit. Otherwise, the system may perform one or more fraud prevention actions to authenticate the customer and either approve or deny the transaction request up to the spend limit and/or selectively perform fraud mitigation action(s).

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Systems and method for linking a social media handle to a financial account and providing a payment handle are disclosed. The payment handle is a unique identifier that allows a social media user to enter the payment handle in a checkout page within the social media ecosystem to authorize payment for goods or services. An account provider system can create a first payment handle to associate with a first social media system and a second payment handle to associate with a second social media system. The payment handle can be generated according to a list of preferred goods, merchant lists, or maximum transaction amounts. Rather than entering a credit card number, expiration date, address, and other data into a conventional checkout page, the system can enable a user to checkout simply by entering the payment handle and hitting “submit” (or similar).