A computer system uses risk-based principles to identify customers who have conducted money laundering activities. A money laundering risk score is derived from transactional information, background information, and/or due diligence information for each customer of a group of customers. Customers with higher money laundering risks are identified and monitored more closely. The computer system also assists a user to identify at least one transaction that has caused the identified customer to have a money laundering risk score that is in the higher percentiles of the customers.

A distributed transaction management computing system is configured for detecting transaction-specific data discrepancies that result from data calculations performed by different processing instances executing on geographically-distributed computing entities. The transaction-specific data discrepancies are identified via a network-accessible operations computing entity in electronic communication with a plurality of geographically-distributed edge computing entities. The operations computing entity defines at least two separate processing instances, wherein a first processing instance is configured to generate secondary data for individual transactions in the event that the edge computing entities do not themselves generate the transaction-specific secondary data; and wherein a second processing instance is configured to generate audit data for individual transactions. The operations computing entity compares the audit data and the secondary data for individual transactions to identify discrepancies therebetween that may arise from differences in data generation methodologies performed by the edge computing entities and the multiple operations computing entity processing instances.

A safe, secure, short-term repository of transactional information collected across financial institutions and stored in a single place, specifically allowing safeguards to be created and executed across accounts is described so as to protect the financial accounts of a vulnerable user. A mobile device application facilitates near-real-time alerts, approvals, and potential geonotifications available for instant action by Guard users. The system allows geographically distant loved ones the ability to Guard the financial transactions of a user from afar. Multi-account withdrawal and transaction safeguards may be implemented, treating multiple financial accounts as a single account as it pertains to an established and imposed multi-account withdrawal limit.

Embodiments provide methods and systems for removing temporal biases in classification tasks. Method performed by server system includes accessing a transaction graph associated with a particular time duration and determining a set of local features and aggregate features associated with each node based on labeled data. Method includes generating via a machine learning model, a set of intermediate node representations associated with each of the plurality of nodes based on the set of local features and the set of aggregate features. Method includes generating via a fraud model and a timestep model, a fraud classification loss, and a timestep classification loss based on the set of intermediate node representations. Method includes determining an adversarial loss value based on the fraud classification loss and the timestep classification loss. Method includes determining a set of optimized parameters for the machine learning model based on the adversarial loss value.

A method for the secure use of a personal identifier and/or financial instrument that may take place simultaneously with traditional methods of authorization for credit card, check, funds withdraw/transfer or purchase. Authorization according to the present invention may take place as follows: A) the Owner provides a pre-approval of authorization if the request meets an array of Owner-defined parameters; B) an authorization may be granted based on proximity of the Owner to the point of use; or C) the Owner provides a real time approval by smart device. In each case the invention subjects the request for authentication to a sequential verification procedure in which the request is tested against one or more pre-defined verification protocols, the deployed authentication protocol being pre-determined by the Owner and pre-selected in accordance with a user profile that was pre-programmed by the Owner.

Embodiments of the disclosure are directed to methods, systems, and devices for the use of portable devices, such as a vehicle driven by a user, that are distinctive physical objects and can be presented to an authentication system for authentication purposes. More specifically, the portable device may be used as a source of information for the authentication system that can be verified. Images may be captured of the portable devices in order to determine their distinctive features for comparison to other physical objects registered to the user. The portable devices may also be a source of metadata, or additional contextual information associated with those portable devices or their interaction with the authentication system. The image data and/or the metadata associated with a portable device can be verified for fraud or risk analysis.

Identification elements may be added to a vehicle and the identification elements may be required before a transaction through the vehicle may be approved. The identification elements may be a second layer of security to ensure that a payment request is valid and not fraudulent.

A system for predicting a non-fraud dispute using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a data access interface to receive instructions historical transaction and disputes data from at least one data source associated with an account issuer. The data access interface may also receive incoming transaction data associated with a transaction from at least one data source associated with an account holder. The system may comprise a processor to predict a likelihood of a non-fraud dispute associated with the transaction by: examining the historical transaction and disputes data; retrieving non-fraud dispute attributes; parsing the incoming transaction data; applying predictive analytics to the incoming transaction data to yield a prediction value; determining that the prediction value meets a predetermined threshold; and generating a prediction for the likelihood of a non-fraud dispute associated with the transaction associated with the account holder to be outputted, via an output interface to a user device.

Parallelized computation by a real-time transaction scoring system that incorporates global behavior profiling of transacting entities includes dividing a global profile computing component of a transaction scoring model of a real-time behavioral analytics transaction scoring system into a plurality of global profile component instances. The transaction scoring model uses a plurality of global profile variables, each of the plurality of global profile component instances using its own global profile partition that contains the estimate of global profile variables and being configured for update by a dedicated thread of execution of the real-time transaction scoring system, each dedicated thread being configured for receiving and scoring a portion of input transactions. The method further includes partitioning, based on one or more transaction routing shuffling algorithms, the input transactions for receipt across the plurality of global profile component instances, and updating each of the plurality of global profile partitions by the corresponding global profile component running in the dedicated thread according to the scoring algorithm.

Systems and methods described herein include first and second devices including one or more processors and memory, the first device being configured to purchase one or more certificates by a first process via one or more blockchain transactions. The second device may be in communication with the first device, and may be redeem the one or more certificates received by a second process via one or more blockchain transactions. The second device may exchange one or more codes to designate the one or more certificates as used on the first device. The system may include one or more servers in communication with the second device, and may be automatically generate a message containing updated redemption information of the one or more certificates when the updated redemption information has been stored in one or more databases, which may be in communication with the one or more servers.