Disclosed herein provides an automated dispute resolution system in data communication with a user device comprising: a memory and a processor, the processor configured to receive identification information, scale a risk of misidentification; record a communication in the memory, together with a timestamp; receive input to components of a matter; collect information on the matter; establish a prediction based on a type of dispute resolution and one or more attributes; create an action item; identify a solution; and present the solution to the matter parties. Further disclosed herein provides a method, comprising: receiving identification information, scaling a risk of misidentification; recording a communication in the memory, together with a timestamp; receiving input to components of a matter; collecting information on the matter; establishing a prediction based on a type of dispute resolution and one or more attributes; creating an action item; identifying a solution; and presenting the solution to the matter parties.

Systems and methods for detecting user account fraud rings are disclosed. In an embodiment, a computer system may access a plurality of user accounts created within a past period. The computer system may generate a tree of user accounts by recursively identifying pairs of user accounts by beginning with a seed account for the tree and iterating through user account pairs at lower branch levels to determine whether each user account has been paired to one or more other user accounts based on respective hard link features and soft link features. If a user account has been paired to one or more other user accounts, the computer system adds the one or more other user accounts to a branch level below the user account in the tree. The user accounts of the tree may be included in a cluster. Actions can be taken against the user accounts in the cluster.

A system and method is provided for automatically creating machine learned fraud detection models. Data received from a plurality of devices can be used to train a model for each of the plurality of entities. Each of the models can be trained using recursive model stacking and each model can output a corresponding score. A second model can be trained for each of the plurality of entities based on the first model and a corresponding output score of the first model. The second model can also be trained using recursive model stacking.

Methods and systems are presented for improved detection of fraudulent activity within a payment system. Methods and/or systems receive one or more seed accounts from among a plurality of accounts, generate a graph based on the one or more seed accounts where the graph includes a plurality of nodes including one or more first nodes corresponding to the one or more seed accounts and a plurality of second nodes corresponding to a plurality of accounts that are associated with the one or more seed accounts, link the related nodes within the graph based on a common attribute shared between a pair of corresponding accounts, identify one or more groups within the one or more communities based at least on a density of connections among the nodes within the one or more communities, and determine, using a machine learning model, a corresponding label for each group in the one or more groups.

A data analysis system includes processor to: arrange data in a multi-dimensional structure based on a target activity defined by a smart card; perform analysis on the data to predict an outcome of the target activity of activities; determine a probability of success of the outcome that has been predicted; determine, based on the outcome and probability of success, choices associated with the activities; determine patterns and changes in the data pertaining to the activities detected by an access device with access to the smart card; perform transformative and scheduling, exposed through an application programming interface for the data; schedule to arrange the choices and the probability of success of the outcome for the access device; cue the choices and the probably of success; and transmit the plurality of choices and the probability of success of the outcome to the access device for rendering on the smart card.

Method for confirming the identity of a user in a browsing session of an online service, comprising the steps of: a) providing a web server in which an online service resides, in communication with a client device provided with a user interface; b) providing a database associated with the web server in which a plurality of data relating to one or more users registered to the online service are stored; c) providing a script residing in the client device; d) identifying via script each browsing session on the online service and associating it with a user registered to the online service when the latter performs authentication; e) collecting via script biometric data generated by said at least one user interface and associating them with the user when authenticated; f) generating via script machine learning templates as a result of processing the biometric data; g) storing the biometric data and the machine learning templates locally in the client device; h) generating a score associated with the user as a result of processing via script new biometric data collected on said at least one user interface as a function of the machine learning templates generated in step f); i) sending the score to the web server; l) verifying the identity of the authenticated user as a result of processing the score by means of a security algorithm residing in the web server.

A user controlled mobile device for use in countering phantom billing fraud in connection with receiving or providing health care services includes one or more components capturing and outputting biometric data and location data, and a data storage device holding an event record created without explicit user intervention indicating whether the particular user was at the particular location, the event record including a timestamp corresponding to events at or near a time of the timestamp including a time of capture of the biometric and location data, the biometric data and location data, where the stored event record serves as the personal audit trail evidencing an existence or absence of phantom billing.

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Methods and systems disclosed herein describe a generic transaction card that generates an account number based on a biometric identifier of a user. Multiple users may use the generic transaction card to transact. For example, a first user may provide a first biometric identifier to the generic transaction card, which may generate a first account number associated with the first user. The first user may then use the generic transaction card to transact. Similarly, a second user may provide a second biometric identifier to the same generic transaction card, which may generate a second account number associated with the second user. The second user may then use the same generic transaction card to make purchases charged to the second user's account.

Cryptocurrencies may be used within the current Four Party Model to settle transactions between a merchant and a consumer who may use or accept fiat and/or cryptocurrency. An intermediary wallet entity may assume the risk of cryptocurrency transactions. For example, a fiat-fiat transaction may follow the usual settlement process of the Four Party Model. In a crypto-crypto transaction, settlement may occur in real time. But in a fiat merchant/crypto customer transaction, settlement may be made from the customer's crypto wallet to an intermediary wallet. Fiat settlement would then occur between the intermediary and merchant following the Four Party Model. In a crypto merchant/fiat customer transaction, a transfer may be made from the intermediary wallet to the merchant's crypto wallet. Fiat settlement between the customer's bank (issuer) and intermediary can happen in the usual settlement process.