This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received. In some examples, the computing device prevents completion of the purchase transaction if the payment transfer is determined to be fraudulent.

Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment. The method further includes replicating the transaction data in the shadow testing environment as input for testing the transaction service using the machine-learning model.

Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

A computing system receives a code that is generated based upon an image of an iris of a first user. The image of the iris of the first user is captured by way of an iris scanning device that is under control of a second user. The computing system executes a search over a plurality of codes stored in a data store based upon the code, where the plurality of codes are generated based upon images of irises of a plurality of users. The computing system determines whether the code is included in the plurality of codes based upon search results for the search. Based upon a determination that the code is not included in the plurality of codes, the computing system causes an amount of cryptocurrency to be transferred to a cryptocurrency wallet of the second user.

An actionable alert platform is provided. The platform may utilize artificial intelligence (“AI”) to formulate alert triggers that inform a user regarding unusual activity associated with a target transaction. The platform allows a user to enter customized criteria for an alert trigger and actionable responses for remediating the detected alert trigger. In response to detecting an alert trigger, the platform may formulate and transmit push notifications to a mobile device of user. The user may implement the actionable response directly from within the push notification. The platform may also provide push notifications to the user that advise on a status of the target transaction after applying the actionable response. An AI engine may generate additional alert triggers based on the criteria entered by the user. The AI engine may generate the actionable responses for any alert trigger.

Systems and methods are disclosed for establishing fraud detection system for authorizing payment for consumer transactions based on a user's tender-switching or transaction abandonment practices. One method includes: receiving transaction history of a user, the transaction history including a first payment vehicle and a second payment vehicle; determining, of the received transaction history, one or more instances of switching from one the first payment vehicle to the second payment vehicle; and determining a user-specific fraud threshold score for the user, based on the determined instances of switching from the first payment vehicle to the second payment vehicle.

A method for verifying origin of request for payments may include a financial institution computer program: receiving a request for a payment URL from a merchant, the payment URL provided for a transaction with a customer; generating the payment URL, wherein the payment URL comprises a pointer to a network location for the financial institution; providing the payment URL to a customer electronic device; receiving, from the customer electronic device and at a browsed URL, a stored or pinned certificate of domain for the payment URL; (5) comparing the stored or pinned certificate of domain for the payment URL to a certificate of domain for the browsed URL; presenting a payment page in response to a match; and returning an error in response to a mismatch.

A method of staging a transaction includes receiving transaction information including a transaction type selection, a send amount, sender and recipient information, and compliance data. A staging record is generated responsive to the transaction information including at least a retrieval key to identify the staging record and a transaction type identifier expressing the transaction type selection. The transaction type selection is detected from the staging record, which is sent to an agent terminal set up to handle the transaction. The method further includes providing instructions for fulfillment of the transaction at the agent terminal. The transaction is rejected if the retrieval key does not lead to the staging record and the transaction proceeds if the retrieval key leads to the staging record. The transaction proceeds if the send amount and compliance data are consistent with applicable compliance rules and the transaction is rejected if the compliance rules are not met.

Mobile communications devices, systems and methods are provided for identifying a risk of fraudulent activity associated with a merchant before a user executes a transaction with the merchant. In an embodiment, a mobile communications device obtains first information identifying prior instances of fraudulent activity associated with a merchant. The mobile communications device may determine a level of risk of fraudulent activity associated with the merchant based on the obtained first information, and may identify, based on the determined risk level, one or more payment instruments and associated incentives appropriate for financial services transactions involving the merchant. The mobile communications device may present, to a user, a notification including the identified payment instruments and associated incentives prior to execution of a transaction between the user and the merchant.

A computer-implemented method for enhancing authorization of prepaid payment cards is provided. The method is implemented using an interchange network and an authorization computing device in communication with a memory. The method includes routing communications between a plurality of merchants and a plurality of issuing banks, providing transaction data for transactions by a plurality of prepaid payment cards, storing a plurality of transaction histories associated with the plurality of prepaid payment cards, receiving, over the interchange network, an authorization request for a payment transaction between a cardholder and an originating merchant initiated using a prepaid payment card, determining a likelihood of a second amount for a related payment transaction from the originating merchant, determining a transaction history associated with the prepaid payment card, determining whether there are sufficient funds associated with the prepaid payment card for the first and second amounts, and notifying the originating merchant of the determination.