A method includes a computing entity interpreting digital records representing a multitude of contingent assets to produce a set of contingent asset sale requests. A first contingent asset of a first contingent asset sale request assigns a potential first liability of a first payer to a first seller.

The method further includes interpreting a first authenticity indicator associated with the first contingent asset sale request to produce a first contingent asset risk level. When the first contingent asset risk level is greater than a contingency risk threshold level, the method further includes establishing first available terms for the first contingent asset based on the first contingent asset sale request, generating a first smart contract to indicate availability of the first contingent asset to include the first available terms, and causing generation of a non-fungible token to represent the first smart contract in an object distributed ledger.

A method may include presenting a user interface (UI), to a user, in a first mode on a client device, the UI including a list portion, the list portion including a plurality of transactions associated with respective activity on a user account, and the client device including an input device; receiving an indication of a first user action on a transaction presented in the list portion of the UI; in response to receiving the indication, updating a section of the list portion corresponding to the transaction, the updating including revealing a set of options for the transaction; receiving an indication of a second user action corresponding to activation of an option of the set of options; and in response to receiving the indication of the second user action, transitioning the UI from the first mode to a second mode, the second mode including a chatbot interface instantiated with respect to the transaction.

An alert management and real-time remediation system is disclosed. The system may receive, from an institution, an electronic file that includes an exception information (EI) data structure comprising exception information representing events and customers associated with the events, and a contact information data structure comprising an electronic address for each customer. The system may automatically parse the EI into events associated with the customers, and for each customer, send an event notification message (EN) to the electronic address of the customer, wherein the event notification message includes a link to an authentication interface of the institution associated with the customer. Responsive to the customer being authenticated by the authentication interface, providing a user interface to the electronic customer device for display to the customer, wherein the user interface displays information for each event associated with the customer and display elements from which a user may select, including a display element for a payment option; and responsive to the customer selecting the payment option, generating and sending a payment message to the institution.

There are provided systems and methods for actionable insight into user interaction data. A service provider server can access user interaction data associated with an interaction between a first communication device and the service provider server, and generates feature representations of the user interaction data, in which the feature representations respectively correspond to extracted features that include textual data features or audio data features. The service provider server can determine an intent of the interaction from the feature representations using a machine learning-trained classifier, in which the intent corresponds to a first actionable insight category. The interaction is mapped to a first cluster based on the intent, and the service provider server issues a remedial action for the interaction based on the mapping of the interaction to the first cluster, in which the remedial action is associated with a particular type of activity in the first actionable insight category.

Examples described herein relate to apparatuses and methods of detecting fraudulent activity at an automated teller machine (ATM) using a machine learning model. A method includes receiving ATM activity data indicative of one or more withdrawal transactions at one or more ATMs using a transaction card, receiving transaction data and ATM data, ingesting the transaction data and the ATM data, analyzing the ingested transaction data and the ingested ATM data using a machine learning model, determining that the ingested transaction data and the ingested ATM data indicate a likelihood of fraudulent activity using the machine learning model, and performing, using the machine learning model, one or more remedial actions based on the determined likelihood of fraudulent activity.

As described herein, a system, method, and computer program are provided for a financial-based recommendation engine. In use, financial-related data is gathered for a plurality of users from a plurality of sources. Additionally, the plurality of users are clustered into a plurality of cohorts, based on the financial-related data. Further, a plurality of user profiles are created for the plurality of cohorts, including for each cohort of the plurality of cohorts, creating a corresponding user profile based on the financial-related data for the users in the cohort. Still yet, each user profile of the plurality of user profiles is matched to one another and to one or more financial products. Moreover, the one or more financial products matched to each user profile of the plurality of user profiles are recommended to the users in the cohort that corresponds to the user profile.

A method includes receiving, by a marketplace server, an acquisition request regarding an exchange item from a computing device, where the exchange item has exchange item information that include a set of use rules that govern utilization of the exchange item. The method further includes obtaining, by the marketplace server, dynamic external factors for the request to acquire the exchange item and determining variable contract function information based on the external factors in accordance with a smart contract approach. The method further includes sending an acquisition response to the computing device that includes the variable contract function information and the exchange item information. The method further includes obtaining, by the computing device, current dynamic external factors associated with the variable contract function information and generating, by the computing device, updated exchange item information for the exchange item based on the current dynamic external factors and the variable contract function information.

Systems, methods, and apparatus for handling and/or authorizing payment requests by a consumer for a transaction are provided. Challenge questions may be presented from the non-merchant to the consumer after receiving the payment information, thereby allowing a Judicious determination of when to ask such challenge questions and allowing complex and probative questions. The time limit for such challenge questions may advantageously be unconstrained, while still preserving security. Also, a risk analysis may be started prior to the consumer submitting payment information, thus showing an efficient and complex risk analysis. Information about an access device used by the consumer may be obtained at the non-merchant from the access device with code sent to the access device from the merchant, and the device information may be used in the risk analysis.

Placing an event into a particular cluster can allow various inferences about the event. A new payment transaction that looks similar to a previously identified cluster of mostly fraudulent payment transactions, for example, may be higher risk. The present disclosure includes structural data improvements to the way that online clustering of events (which may include web events and not just payment transactions) occurs. A new event can be classified into a particular segment very quickly using feature table searching, which can allow for better decision making when a short timeframe is required (e.g. transaction processing, online advertising, etc.).

A computer-implemented method for real-time transaction fraud vetting, including: receiving a transaction record including real-time transaction data and a key identifying a transacting entity; matching the key to a plurality of profiles of the transacting entity, each of the plurality of profiles including a data attribute; accessing a datapoint for each of the plurality of profiles, each datapoint representing a standard for the corresponding data attribute computed from historical transaction records of the transacting entity; assessing whether deviation of the real-time transaction data from each datapoint exceeds a corresponding threshold; incrementing a transaction risk corresponding to the transaction record for each deviation from one of the datapoints that exceeds the corresponding threshold; outputting a fraud score based at least in part on the transaction risk; and updating at least one datapoint based on the corresponding real-time transaction data to generate an updated datapoint set for the plurality of profiles.