Abstract: Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

Abstract: Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

Abstract: Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

Abstract: A method includes: communicating data from a browser, operating on a computing device, to an electronic shopping cart during a transaction; receiving user-authenticating data from a user interface of the computing device; decrypting secure user data stored to the computing device; authenticating a user based on the user-authenticating data and the decrypted secure user data; in response to authenticating the user, transmitting an indication of payment from the browser to the electronic shopping cart; processing the transaction after transmitting the indication of payment; and recording the transaction in a blockchain stored on the computing device.

Abstract: Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

Abstract: A method includes: communicating data from a browser, operating on a computing device, to an electronic shopping cart during a transaction; receiving user-authenticating data from a user interface of the computing device; decrypting secure user data stored to the computing device; authenticating a user based on the user-authenticating data and the decrypted secure user data; in response to authenticating the user, transmitting an indication of payment from the browser to the electronic shopping cart; processing the transaction after transmitting the indication of payment; and recording the transaction in a blockchain stored on the computing device.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.

Abstract: A method includes: communicating data from a browser, operating on a computing device, to an electronic shopping cart during a transaction; receiving user-authenticating data from a user interface of the computing device; decrypting secure user data stored to the computing device; authenticating a user based on the user-authenticating data and the decrypted secure user data; in response to authenticating the user, transmitting an indication of payment from the browser to the electronic shopping cart; processing the transaction after transmitting the indication of payment; and recording the transaction in a blockchain stored on the computing device