Abstract: Techniques are disclosed relating to receiving, by a computer system from a user, a request to authorize the user. The technique may further include receiving, by the computer system, a live video stream of the user, and requesting, by the computer system, the user to submit an identification document from a list of authorized identification documents. While the user submits the identification document, the technique may also include detecting liveliness of the user in the live video stream by using a gesture recognition operation. Additionally, the technique may include determining, using the submitted identification document and the detected liveliness, whether to proceed with the request to authorize the user.

Abstract: There are provided systems and methods for data privacy protection and removal for artificial intelligence model training and deployment. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include use of machine learning (ML) models including large language models (LLMs). To comply with data privacy protections and copyright enforcement, a system may provide unlearning of content from ML models. The system may receive a request to unlearn a content and, after verifying the request is valid, identify the content used for during training of or inferencing by an ML model. The system may then map the content to concepts and correlate those concepts with ML model outputs using projections in a vector space. Based on the mapped concepts and outputs, neuron activation of the ML model may be analyzed to identify a negation vector and perform selective parameter dampening.

Abstract: Systems and methods for managing threats in a network including applying, at a first layer, a first request obtained from a first computing device to a first graph to determine one or more first embeddings and one or more second embeddings, classifying, at a second layer, a second request obtained from a second computing device as a first request type or a second request type based on applying the second request to a second graph, predicting, at a third layer, an authenticity of a call sequence obtained from the second computing device based on a sequence threshold, and sending the call sequence to a third computing device based on authenticating the call sequence. The first request is a transaction to be performed by the second computing device and the second request is a second processing transaction to be performed by the third computing device based on the first request.

Abstract: Systems and methods for managing threats in a network including applying, at a first layer, a first request obtained from a first computing device to a first graph to determine one or more first embeddings and one or more second embeddings, classifying, at a second layer, a second request obtained from a second computing device as a first request type or a second request type based on applying the second request to a second graph, predicting, at a third layer, an authenticity of a call sequence obtained from the second computing device based on a sequence threshold, and sending the call sequence to a third computing device based on authenticating the call sequence. The first request is a transaction to be performed by the second computing device and the second request is a second processing transaction to be performed by the third computing device based on the first request.

Abstract: A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.

Abstract: A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.

Abstract: A method may include receiving, via an interface respective of a third party, a first request respective of a user to access a payment application, prompting the user, in response to the first request, to provide user credentials, receiving, from the user, user credentials, processing, via a first set of modules, the user credentials to determine a validity of the user credentials, in response to determining that the user credentials are valid, retrieving transaction details from the third party, the transaction details comprising a profile of the third party and a profile of a subject of the transaction, processing, via a second set of modules, the transaction details to determine a validity of the transaction details, and in response to determining that the transaction details are valid, transmitting an approval of the user to the third party.

Abstract: A computer-implemented method for utilizing a machine learning model configured to determine synthetic identity theft may include processing a plurality of user datasets to generate a set of features for each user dataset, with each set of features being representative of a particular user. The method may further include generating a plurality of embeddings sets, with each embedding set being representative of a respective set of features, generating a plurality of synthetic user datasets, combining the plurality of embeddings sets and the plurality of synthetic user datasets to generate a training dataset, the training dataset comprising a plurality of user profiles, training the machine learning model based on the generated training dataset, and determining, via the machine learning model and in response to receiving a new user profile, a determination of whether the new user profile is real or synthetic.

Abstract: A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.

Abstract: A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.

Abstract: Techniques are disclosed relating to receiving, by a computer system from a user, a request to authorize the user. The technique may further include receiving, by the computer system, a live video stream of the user, and requesting, by the computer system, the user to submit an identification document from a list of authorized identification documents. While the user submits the identification document, the technique may also include detecting liveliness of the user in the live video stream by using a gesture recognition operation. Additionally, the technique may include determining, using the submitted identification document and the detected liveliness, whether to proceed with the request to authorize the user.