Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

A method may include generating a vector from unstructured data included in an untransformed transaction, and determining, for the vector, a cluster ID of cluster IDs by matching the vector with a matching cluster vector of cluster vectors. The method may further include generating a query using the cluster ID and the untransformed transaction, and transforming, using the cluster IDs, untransformed transactions to transformed transactions. The transformed transactions may each include a cluster ID. The method may further include generating, using the query, a query result from features of the transformed transactions, generating a fraud score using the query result, and presenting the fraud score and the cluster ID.

Methods and systems for generating a universal computer model for assessing a risk in an electronic transaction based on one or more risk assessment models are presented. The one or more risk assessment models may be incompatible with each other. Different portions of a risk assessment models may be extracted from the risk assessment models. A node structure is generated for each risk assessment model based on the portions extracted from a corresponding risk assessment model. The node structures generated based on the risk assessment models are merged to produce a merged node structure. The universal computer model is generated based on the merged node structure.

Systems and methods are described for facilitating payments and transactions using social background checks. Such systems and methods may use social networks with both individual members communicating over a network to a social authentication computing system. The authentication of transactions associated financial institutions are determined through the use of relationship measures based on social media interactions. Transactions and services available to a user are determined based on a measure of social identity through the use of social media platforms. Available contact and interaction data from one or more social media platforms is leveraged to analyze a level of trust that a transaction is not a consequence of fraudulent activity. Transactions that have a low level of risk of being a consequence of fraudulent activity are benefited through faster transaction times and other improvements.

Methods, systems, and apparatuses are described herein for authenticating access to an account using questions relating to which user, of a plurality of users authorized to access the account, performed certain actions. A request for access to an account may be received. Transaction data for the account may be received. A list of merchants may be generated for at least one transaction. An authentication question relating to the identity of a user that conducted a transaction may be generated. For example, the authentication question may prompt the user to indicate which authorized user(s) conducted particular transaction(s). The user device may be provided the authentication question. A response to the authentication question may be received. Access to the account may be provided based on the response.

Aspects described herein may allow for authenticating a user by generating a customized set of authentication questions based on spending patterns that are automatically detected and extracted from user data. The user data may include transaction data collected over a period of time that may indicate the types of merchants that a user frequently transacts with. By automatically detecting user patterns that correspond to user behavior over a period of time, an authentication system may be able to generate authentication questions about those spending patterns that are easily answerable to an authentic user but difficult to guess or circumvent for any other user.

Systems and methods for authorizing a blockchain transaction. A verification network receives a transaction request for the blockchain transaction from a payer device including a first signature generated by a first private key associated with a payer. The verification network broadcasts a verification request to verification system(s) which assess pre-agreed threshold parameters. If the parameter(s) are satisfied, at least one verification system perfects the transaction by generating a second signature using a second private key, and broadcasts the transaction to the blockchain network. If the parameter(s) are not satisfied, verification offer(s) from among the verification system(s) including the second signature(s) are used to prompt the payer device to confirm the blockchain transaction by selecting at least one of the offer(s). The verification network receives selected offer(s) from the payer device and broadcasts the transaction to the blockchain network, in accordance with the selected offer(s) and the transaction request.

As described herein, a machine learning system, method, and computer program are provided for making payment related customer predictions using remotely sourced data. A system of a communication service provider (CSP) identifies a customer of the CSP. Additionally, the system collects data from a plurality of data sources independent from the CSP, the data including telephone numbers and/or webpage URLs of other services providers that are associated with making payments. Further, the system processes the collected data to form input data indicating which of the telephone numbers were contacted by the customer and/or webpage URLs were accessed by the customer. Still yet, the system processes the input data using at least one machine learning algorithm to make at least one payment related prediction for the customer. Moreover, the system outputs the at least one payment related prediction made for the customer.

An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

A system and method for managing access to offerings of online digital collectibles. Access may managed according to various eligibility criteria related to a user. The eligibility criteria may relate to aspects of the user's participation, involvement, holdings, or actions in the system. To determine whether a user is eligible for a collectible offering, the system may determine eligibility criteria associated with the offering and obtain from a user's account the information necessary to determine if the user meets the eligibility criteria. The criteria may include a measurement of the digital collectibles currently in the user's account, prior purchase activity by the user, and/or other user-related information tracked by the system.