Methods and apparatus, including computer programs encoded on computer storage media, for monitoring resource transfer are provided. One of the methods includes: by a server, receiving a resource deposit request from a resource deposit initiator; performing, using a first risk identification model, a first risk identification on the target account according to the resource deposit request to obtain a first risk identification result; receiving a resource withdrawal request from a resource withdrawal initiator, and the resource withdrawal request requesting a resource withdrawal from the target account to the recipient account; performing, using a second risk identification model, a second risk identification on the target account according to the resource withdrawal request to obtain a second risk identification result; and determining, using a third risk identification model, a resource transfer risk monitoring result of the target account according to the first risk identification result and the second risk identification result.

One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

Embodiments described herein provide a user interface for interacting with a smart contract deployed to a blockchain. The user interface is custom-generated for each party to the smart contract. In particular, representations of actions that a particular party is enabled to perform and/or has already taken with respect to the smart contract are provided to that party via a user interface. A user experience generator queries the smart contract itself to determine which representations are to be provided to each party. The smart contract comprises functions and/or user experience annotations that indicate to the user experience generator which user interface representations should be provided to a particular user based on the party identity and/or the smart contract's state. The smart contract provides an event notification to the user experience generator, which prompts the user experience generator to query the smart contract to determine the allowed actions for a particular user.

A user mobile device includes a network interface, an input/output (“I/O”) device configured to exchange data with a user, the I/O device including a display device configured to present a graphical user interface to the user, an imaging device configured to generate image data; and a processing circuit. The processing circuit comprising a memory and a processor. The memory stores instructions that cause the processor to receiver user photograph data including multiple photographs of the user, determine an emotive state of the user in each of the photographs, categorize each photograph into at least a user positive emotion category and a user negative emotion category, receive spending behavior data, generate a user spending alert based on the spending behavior data including a photograph from at least one of the user positive emotion category and the user negative emotion category, and present the spending alert to the user.

Aspects described herein relate a computer system detecting an unusual activity event from activity data and sending a two-way notification message to a wireless device when a user has opt-in for a mobile application. The two-way push notification message is received at a wireless device and notification information is displayed at the wireless device outside the mobile application. The user selects one of the displayed action buttons to indicate whether the unusual activity event is valid or invalid. When a response to the computer system indicates that the unusual activity is valid, the computer system clears the inquiry and sends a subsequent push notification message to the wireless device informing the user. When the response indicates that the unusual activity is invalid, the computer system and the wireless device may establish an interaction through the mobile application in order to resolve the unusual activity event.

A method and system capable of generating a relying party risk-adjusted indicator associated with and used by users and parties entering into one or more transactions with a relying party.

A financial institution computing system includes a network circuit exchanging information over a network, a customer database storing financial information, and a transaction circuit. The transaction circuit receives a transaction request associated with a user computing device. The transaction request specifies a financial account. The transaction circuit receives payment card location information indicative of a location of a payment card with respect to the user computing device. The payment card location information indicates that the payment card is within a threshold distance of the user computing device. The transaction circuit authenticates the transaction request using the payment card location information and by determining whether the payment card is associated with an authorized user of the financial account. The transaction circuit authorizes the transaction request based on whether the transaction request is authenticated and transmits a confirmation to a transaction terminal over the network via the network circuit.

A system and method for mobile device verification for an electronic application before providing a digital pass to an approved customer is disclosed. The method and system receives, at a store's customer credit application computer, an application for a new account, the application including a contact information for a customer's mobile device providing, from the store's customer credit application computer, the contact information for the customer's mobile device to a verification system to authenticate the customer's mobile device providing, from a credit account provider, a text to the customer's mobile device, the text causing the customer's mobile device to be subjected to a second authentication; receiving, at the customer's mobile device, a message that includes a link to the digital pass; and utilizing the link to receive the digital pass at a mobile wallet of the customer's mobile device.

A system includes one or more data sources. Each data source is configured to collect and store event data. An event bus is configured to monitor the event data of the one or more data sources. The event bus determines that a first portion of the monitored event data satisfies a first task criteria. The first portion of the monitored event data is associated with a first tag that corresponds to the first task. The event bus determines that a second portion of the monitored event data satisfies a second task criteria. The second portion of the monitored event data is associated with a second tag corresponding to the second task. The first portion of the monitored event data with the first tag and the second portion of the monitored event data with the second tag are provided for storage in a data lake.

In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.