The present disclosure relates to a method and apparatus for acquiring learning data to generate a machine learning model for detecting a scam account of cryptocurrency. The method comprises receiving a report related to a scam address from a first database having information about a reported scam address stored therein, acquiring a first scam address and a first description related to the first scam address from the report, extracting a plurality of first keywords related to the first scam address from the first description using natural language processing, and storing the first scam address in a second database.

Methods, systems, and apparatuses are described herein for improving the accuracy of authentication questions using transaction limitations provided by users. A request for access to an account associated with a user may be received from a user device. An account restrictions database may be queried to determine one or more transaction rules associated with the account. The one or more transaction rules may have been created by the user and indicate limitations on financial transactions that may be performed via the account. An authentication question may be generated that is associated with a violation of the one or more transaction rules. The authentication question may be provided to the user device, and a response to the authentication question may be received. Access to the account may be provided to the user device based on the response.

Methods and systems are described herein for providing an evaluation system and an improved user interface to assist with decision making regarding particular operations. The evaluation system may collect, collate, store, and evaluate information from multiple sources and data of multiple types. The improved user interface may acquire, store, process, and display such relevant information efficiently and compactly for making an authorization decision. An evaluation system uses this data to create assessment scores for multiple people being evaluated for a variety of factors. The combined scores are displayed on the improved user interface for enabling improved visualization to decide if a particular operation is approved or denied.

Disclosed are methods, systems, and non-transitory computer-readable medium for resolving automated teller machine (ATM) errors. For instance, the method may include receiving error information indicative of the error; collecting interaction information, including user information and terminal information, related to the interaction at the terminal; and generating a claim that includes an identification of the claim, the error information, and the interaction information. The method may further include transmitting an indication of the claim to a device associated with the person; analyzing the claim by using a risk exposure model; and selecting, using the analysis of the claim, an exposure value for the claim. The method may continue to determining, based at least in part on the exposure value, whether a claim restitution should be completed; and transmitting, to the device associated with the person, the determination of whether the claim restitution should be completed.

Techniques for a secure blockchain supply management system are disclosed. A blockchain node may load a contract to a blockchain rules engine and invoke a blockchain transaction associated with the contract document. The blockchain node may determine an ordered block of one or more other transactions. The blockchain node may provide the blockchain transaction associated with the contract document. In some cases, the blockchain node may authorize the blockchain transaction associated with the contract document based at least in part on one or more blockchain policies. Also, the blockchain node may install the blockchain transaction associated with the contract document.

A system is provided for estimating quantile values for fraud assessments. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include capturing one or more first data values for a quantile value profile associated with an entity, wherein the quantile value profile includes one of real values or a first plurality of quantile marker values calculated from the real values, accessing the quantile value profile for the entity, determining a first number of the one or more first data values, and based on the first number of the one or more first data values and the one of the real values or the first plurality of quantile marker values in the quantile value profile, performing one of a first merge operation, a second merge operation, or a third merge operation.

Aspects of this disclosure relate to use of a monitoring platform in an electronic fund transfer network for detection of fraudulent fund transfers. The monitoring platform may use a database of accounts known to be associated with malicious activity in combination with an ML engine for the detection. The ML engine may be trained, using supervised machine learning, to identify fraudulent fund transfers based on various parameters associated with fund transfer requests. The monitoring platform may review the requests in near real-time and cancel or recall fraudulent requests.

Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score. Further, the method includes transmitting a recommendation message to an issuer server associated with the cardholder based on the account-level risk scores.

A method and system for acquiring an Internet user's consent to be geographically located via at least two independent sources of geographical information. At least one independent source of geographical information is the wireless location of the Internet user's mobile phone.

A method for execution by one or more computing devices of an exchange item marketplace network includes generating, by an exchange item origination entity, an origination block of an exchange item transactions chain associated with an exchange item, where a portion of the origination block is encrypted. The method further includes receiving, from a computing device of the exchange item marketplace network, an acquisition request regarding the exchange item. The method further includes generating a first block of the exchange item transactions chain, where the first block includes information regarding the exchange item, the computing device, and the acquisition request, where a first portion of the first block is encrypted, and where the first block is chained to the origination block in accordance with a security approach.