Decentralized computer systems and methods are disclosed for analyzing transaction attributes and dispute information using blockchain. One method includes: receiving a first transaction information of a first transaction, wherein the transaction information includes attributes of the first transaction; generating a blockchain entry that stores the received attributes in a shared ledger to participants of the blockchain; receiving an input of a selection of an attribute of the first transaction from a participant of the blockchain to analyze other transactions having the selected attribute; compiling a list of other transactions having the selected attribute; receiving transaction information for each transaction of the list of other transactions; and generating, on a user interface, an analytics report of the attributes of the other transactions.

Systems, methods, and computer programming products for detecting, clearing and/or separating potentially tainted digital assets entering a digital network, providing an avenue to satisfy regulatory requirements and ensure digital asset trading is uninterrupted due to the presence of tainted assets. Specialized nodes of the blockchain network intercept incoming transactions and routes the digital assets to a clearing house for analysis, processing and separation of potentially tainted assets. The clearing house uses a plurality of techniques to track, trace and analyze digital assets using techniques provided by the blockchain network or utilizing one or more service providers. Cleared assets are processed and channeled from the clearing house to the wallets or addresses designated by the transaction information and assets with high potential for being tainted are placed within a special wallet for additional clearance and regulatory scrutiny.

Methods and systems are provided for utilizing interactive UI dialogs to resolve issues that may arise during the real-time ordering and activation of wireless products and services while a user is engaged during their session. The interactive UI dialogs may provide a customer with an opportunity to provide information, make decisions or trigger additional steps during the real time order.

In various embodiments, a system, method, and computer readable medium of the present disclosure comprises authenticating, by a processor of a computing device, credential information of a user of the computing device; releasing, by a processor of the computing device and in response to authenticating the credential information, a first secret element to a secure element processor of the computing device; verifying, by the processor of the computing device from the secure element processor of the computing device, that the user is an authorized user of a first transaction account by comparing the first secret element with an issuer element associated with the first transaction account; and activating, by the processor of the computing device, the first transaction account for a transaction on the computing device.

A technique for remote access via a computing device is discussed. In one embodiment, a computing device receives data indicative of a card number associated with a bank-issued debit card. The debit card is associated with a bank account at a bank that issued the bank-issued debit card. The computing device also receives data indicative of an amount of cash to be deposited into the account based on an amount of cash provided at the computing device location. Encrypted data indicative of the card number and the data indicative of the amount of cash is routed to a gateway vendor server and is further routed to a payment network server for deposit of an amount corresponding to the data indicative of the amount of cash into the bank account associated with the card number.

Disclosed herein are system, method, and computer program product embodiments for authenticating a mobile user via an authentication method determined based on a token level associated with the action being completed. An authentication token is created corresponding to the token level and the authentication token is sent to the mobile device. This authentication token may be used to authenticate subsequent actions and engage various services to complete the actions using application programming interfaces. The authentication token stored on the mobile device obviates the need for a user to authenticate multiple times to complete actions requiring a similar token level. The system may authenticate the identity of the mobile user using various authentication methods.

Provided is a computer-implemented method for generating a machine learning model to classify an account based on merchant activation, including providing an input to a generator network of a generative adversarial network (GAN) to generate an output; providing the output as input to a discriminator network; providing a training dataset as input to the discriminator network; and updating the generator network based on a first output of the discriminator network having a label that indicates whether a set of values of each of the plurality of features is a real set of values or a fake set of values. The method may include updating the discriminator network based on a second output of the discriminator network having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction. A system and computer program product are also provided.

Various embodiments described herein are generally directed to techniques for managing fraud rules and generating authentication directions according to location data from one or more client devices. Embodiments may include using a payment server informed by a client device application to manage and/or apply fraud rules for transactions. In embodiments, fraud rules may be managed based on location data provided by a client device application and/or location data associated with at least one transaction system. In some embodiments, fraud rules may be managed based on location data provided by multiple client device applications and/or location data associated with at least one transaction system. In some embodiments, rules may be managed for an individual authorization request according to information from at least one client device application and location data for a transaction system associated with the authorization request.

Disclosed herein are system, method, and apparatus for detecting a skimmer via a vibration sensor on a transaction card. The method includes receiving, at a server, from a user equipment, vibration information recorded by a transaction card in response to an execution of a transaction at a point-of-service (POS) terminal of a plurality of POS terminals using the transaction card. The vibration information may be recorded by a microphone or an accelerometer on the transaction card while the transaction card is swiped through the POS terminal or inserted into or removed from the POS terminal. The method includes determining, at the server, a state of the POS terminal based on the received vibration information and in response to a determination of the state of the POS being compromised, sending to one or more stakeholders of the transaction a warning message containing the POS terminal information including the POS terminal’s state.

The present disclosure provides a method and apparatus for iterating a credit scorecard model, electronic device, and storage medium, and relates to the technical field of machine learning, in particular, to the field of smart finance and risk control technologies. A method can include: dividing multiple to-be-classified samples into a first part sample and a second part sample; classifying the first part sample by using a first credit scorecard model to obtain a first prediction result, and classifying the second part sample by using the first credit scorecard model to obtain a second prediction result; and determining an input sample of a second credit scorecard model based on the first prediction result and the second prediction result, and iterating the first credit scorecard model according to the second prediction result and multiple history samples, to obtain a target credit scorecard model.