The present disclosure provides systems and methods for computerized methods and systems for processing transaction disputes and processing transactions associated with compromised accounts. According to one exemplary method, a server receives, from a user device, an instruction for disputing a transaction associated with a user account. The server can initiate, based on a record of the transaction, a dispute investigation, and issue provisional credit to the user account for the transaction. Based on a transaction history associated with the user account, the server can determine a plurality of expected transactions associated with the user account and cause the determined expected transactions to be displayed on the user device. After receiving a transaction authorization for at least one of the plurality of expected transactions, the server can process payments for the at least one of the plurality of expected transactions, and generate a notification indicating a status of the dispute investigation.

Systems and methods for tokenization, management, trading, settlement, and retirement of renewable energy attributes are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for tokenization, management, trading, settlement, and retirement of renewable energy attributes may include: (1) identifying renewable energy generation data associated with a seller to tokenize; (2) generating at least one renewable energy token for the identified renewable energy generation data; (3) writing the renewable energy token to a first distributed ledger; (4) adding the renewable energy token to a digital wallet for the seller; (5) exchanging the renewable energy token for a cash token owned by a buyer by associating the renewable energy token with the buyer and associating the cash token with the seller on the first distributed ledger; and (6) retiring the renewable energy token.

Systems and methods ingest extensive data regarding real-time transactions to determine correlation between a variety of parameters and possible fraud events. Possible fraud can be predicted, and notifications delivered to a fraud team. Correlations can be discovered using unsupervised machine learning, and particular systems and methods can utilize natural language processing to both receive and provide information concerning fraud risk and countermeasures.

One embodiment of the invention is directed to a method comprising, receiving an authentication request message for a transaction. The method further comprises determining that detailed device information is required to authenticate the transaction and generating a message including an identifier and a request for the detailed device information. The method further comprises retrieving the detailed device information from a remote server computer using the identifier and modifying the authentication request message to include the detailed device information. The method further comprises sending the modified authentication request message to an access control server computer. The method further comprises receiving an authentication response message from the access control server computer including a verification value for the transaction, where the verification value is generated based on a result of a risk analysis performed using the detailed device information.

An object of the present invention is to provide an authorization device that enables payment approval processing according to a country where a communication terminal is located. An authorization server 100 includes a customer information database that stores a card number of a credit card being a payment means and a communication terminal 300 being a user terminal in association with each other. A location based authorization determination unit 102 determines legitimacy of a payment with a credit card on the basis of whether location country information based on location registration of the communication terminal 300 when the payment with the credit card is made and country information (payment country information in an authorization request) based on a location of an affiliated store (store) where the payment with the credit card is made satisfy a predetermined condition or not. A authorization control unit 101 transmits a determination result about legitimacy of the payment to an affiliated store 200.

A method of determining fraud includes: receiving a transaction request associated with a first payment transaction between a merchant and a user from a merchant system; generating a first risk score based on the transaction request and a first set pot of transaction data received prior to the transaction request; processing a transaction request approval based on the first risk score not satisfying a first threshold; receiving a risk score request associated with the first payment transaction, where the risk score request is received after the transaction request has been approved; generating a second risk score based on a second set of transaction data received after the first risk score is determined; and automatically classifying the first payment transaction as potentially fraudulent in response to determining that the second risk score satisfies a second threshold.

This disclosure describes aspects of an efficient blockchain API communication mechanism that reduces the energy usage and data usage. In some examples, a publish-subscribe mechanism is used for completed transaction receipts for blockchain transactions of a blockchain. The publish-subscribe mechanism uses an open source remote procedure call protocol or hypertext transfer protocol (HTTP). Components of a distributed blockchain application use a single transport or communications protocol for both synchronous and asynchronous communications.

Techniques are disclosed in which a computing device repeatedly trains, using a stream of user data received at the computing device, a baseline model to generate a device-trained model, wherein the baseline model is trained at the computing device without providing user data included in the stream to a server computer system. In some embodiments, the computing device inputs, to the device-trained model, a set of characteristics associated with a user request received from a user of the computing device, wherein the device-trained model outputs a score for the user request. In some embodiments, the computing device transmits, to the server computer system, the score for the user request, wherein the transmitting includes requesting a decision for the user request. In some embodiments, the computing device performs an action associated with the user request in response to receiving a decision for the user request from the server computer system.

Novel technical ways of analyzing a blockchain system using machine learning are presented, including structures and techniques that can facilitate blockchain address risk assessment via graph analysis. In various embodiments, a system can access a blockchain. The system can build a transaction graph based on analysis of the blockchain. Nodes of the transaction graph can respectively represent blockchain addresses that are recorded in the blockchain. In various cases, edges in the transaction graph can respectively represent blockchain transactions between different ones of the blockchain addresses that are recorded in the blockchain. The system can calculate risk scores respectively corresponding to the blockchain addresses, based on analyzing the transaction graph via at least one machine learning algorithm. These techniques can improve computer operational efficiency by avoiding the execution of unnecessary blockchain transactions.

A method for detecting collusive transaction fraud includes: generating a merchant baseline including a transaction data baseline and a time series baseline; extracting time series data of the first merchant system; generating a first score and second score with a deep learning model; generating a first merchant risk score of the first merchant system based on the first and second scores; in response to determining that the first merchant risk score satisfies the threshold, determining a plurality of related entities related to the first merchant system; and classifying the first merchant system and at least one related entity of the plurality of related entities in a first group risk class based on at least one risk score of the at least one related entity.