Abstract: The system may be configured to perform operations including receiving a transaction history comprising transaction information associated with a plurality of transactions for an entity; analyzing transaction information associated with a transaction of the plurality of transactions; and detecting a leakage event associated with the transaction. Detecting the leakage event may comprise determining a payment method for the transaction in response to the analyzing the transaction information; and comparing the payment method to a desired payment method, wherein the leakage event is detected in response to the payment method differing from the desired payment method.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: At least some embodiments are directed to an exemplary computer-based electronic activity tracking system that detects activity patterns receiving data values that represent at least one electronic activity. The exemplary electronic activity tracking system includes a detector of unsecure electronic activities that identifies electronic activity patterns performed by a user or non-person entity. The detector of unsecure electronic activities utilizes unsupervised machine learning techniques to detect the electronic activity patterns. The detected electronic activity patterns correspond to unsecure or malicious electronic activities. The electronic activity tracking system outputs notifications indicative of identified unsecure or malicious activity patterns and identifies entities associated with such unsecure or malicious activity patterns. The exemplary electronic activity tracking system implements a graphical user interface operated from a client computing device.

Abstract: Systems and methods for presenting a personalized value added offer during an advanced verification process are provided. An access control server may receive an enrollment verification request comprising data relating to an account holder. The server may determine an enrollment status of the account holder and determine a value added offer based on the account holder. The server may display an advanced verification interface for user input. The advanced verification interface may include the value added offer and/or a security credential prompt. The server may personalize the advanced verification interface and/or the value added offer based on monitoring the system and compiling historical transaction data.

Abstract: A reward points transfer system and process using blockchain is disclosed. Transaction account issuers may write reward transfer messages to the blockchain to initiate a transfer of reward points for a transaction account holders account to one or more rewards partners. Rewards partners may retrieve the reward transfer messages from the blockchain, and write a reward transfer response message to the blockchain to confirm receipt of the reward points transfer and to update the status of the transfer. The transaction account issuers and rewards partners may encrypt the messages and generate hashes based on the messages prior to writing to the blockchain, to ensure that the messages are not tampered with during the transfer process.

Abstract: Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Naïve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

Abstract: Systems and methods for the calculation of a dynamic trust score are disclosed. The dynamic trust score may indicate a likelihood that the consumer will complete the transaction in a positive manner. The system may calculate the dynamic trust score based on various static and dynamic variables including digital identity data, internal data, third-party data, private data, and/or data from the transaction initiated by the consumer.

Abstract: A consumer may initiate a transaction using a transaction account. The transaction account issuer may transmit a notification to the consumer that the transaction has been authorized. The consumer may select requestees with whom to split the transaction. The transaction account issuer may transmit notifications to requestees to request payment from the requestees. The transaction account issuer may transfer the payment from a transaction account of the requestee to the consumer's transaction account.

Abstract: The systems, methods and computer program products (collectively “system”) described herein relate to customized real time data delivery. The system may be configured to receive, by a performance marketing cluster, first data from a first data source. The system may also receive, by the performance marketing cluster, second data from a second data source. The system may determine, by the marketing cluster, an analysis scheme for the first data and the second data based on the first data source. The system may also determine, by the marketing cluster, at least one of a propensity to act or a recommendation selected from a predefined number of available options for a population based on the analysis scheme and the first data source.

Abstract: Disclosed are examples of transaction cards incorporating aluminum or aluminum alloys. The aluminum can be extracted or recycled from a retired aircraft. Other materials can also be incorporated into the transaction card to provide sufficient weight and rigidity to the transaction card. Stainless steel can be incorporated into the construction of the card in combination with aluminum to provide a desired user experience.

Abstract: Systems and methods for motion-enabled transactions are disclosed. The system may allow users to complete transactions with merchants via an air sign input. The system may receive a motion-enabled transaction request comprising an air sign input. The system may determine a stored air sign symbol based on the air sign input. The system may retrieve a merchant identifier, a transaction type, and a transaction cost based on the determined stored air sign symbol. The system may transmit a transaction confirmation comprising the merchant identifier, the transaction type, and the transaction cost. In response to the user confirming the transaction confirmation, the system may authorize and process the transaction.

Abstract: A blockchain-based multi-merchant loyalty point partnership system may include a blockchain API host that receives a request to create a partnership smart contract for a multi-merchant loyalty point partnership. The request may specify various partnership parameters to include in the partnership smart contract. The blockchain API host may create the partnership smart contract and write the partnership smart contract to a blockchain. One or more qualified merchants may join the partnership smart contract to make the partnership active. In response to a customer completing a plurality of purchases that complete the partnership parameters of the partnership smart contract, the system may issue the customer a purchase reward which may include a loyalty point payout.

Abstract: Disclosed herein are system, method, and computer program product embodiments for mapping API dependencies for a journey test. An embodiment operates by receiving, at a graphical user interface (GUI), a test script representing the user journey within an application, wherein the test script specifies a sequence of application programming interfaces (APIs) that have been called as a user journeys through the application. The embodiment generates an API dependency map within the GUI comprising a sequential map of visual objects, illustrating (1) each of the APIs specified in the test script and (2) any dependent API called by each of the APIs. The embodiment executes the test script representing the user journey. The embodiment generates a status corresponding to each of the APIs called when executing the test script. The embodiment causes display of the generated status of an API or dependent API within the GUI selected by the user.

Abstract: The system may be configured to perform operations including receiving a transaction history for a consumer having transaction information associated with a plurality of transactions; detecting within the transaction information for each transaction a characteristic, resulting in a plurality of characteristics; calculating a respective value associated with each characteristic, wherein the respective value is at least one of a number or percentage of transactions having the characteristic; assigning a respective weight to each characteristic, producing an assigned respective weight for each characteristic; applying the assigned respective weight to the respective value associated with each characteristic to produce a respective weighted value for each characteristic; combining the respective weighted values of the plurality of characteristics; and/or producing a compliance score in response to the combining the respective weight values.

Abstract: Systems and methods for peer-to-peer secure document exchange are disclosed. The system may allow a document provider to securely transmit a certified document to a document verifier using decentralized storage. The verifier system may generate a session key pair and transmit the session public key to a trusted API provider. The trusted API provider may generate a session nonce. The verifier system may transmit the session nonce to the provider system. The provider system may use the session nonce to retrieve the session public key. The provider system may encrypt a certified document using the session public key and store the encrypted certified document in the decentralized storage. The verifier system may retrieve the encrypted certified document by polling the trusted API provider based on the session nonce. The verifier system may decrypt the encrypted certified document using the session private key.

Abstract: Disclosed are various embodiments for using distributed ledgers to assist in securely developing applications. An application component comprising a component file can be received from a validation client. It can then be determined that the application component complies with a security policy. In response to a determination that the application component complies with the security policy, an endorsed application component record can be generated that comprises the component file or a network address at which the component file is obtainable and a signature for the component file generated with an asymmetric key-pair stored in the memory. The endorsed application component record can then be stored in the distributed ledger.

Abstract: Systems and methods for secure mobile checkouts are disclosed. The system may include a secure mobile checkout device configured to scan products available in a merchant store. The device may scan the product to determine a product identifier. The device may transmit the scanned product data to a checkout application to retrieve product data corresponding to the product identifier. In device may initiate a transaction with a payment network to purchase the product, based on the product data provided by the checkout application. The payment network may process the transaction, and may authorize the purchase with the merchant.

Abstract: A system for customer defined limited use authorization. The system may receive an identification credential from at least one of an issuer native app or an issuer web app and receive a customer data. The system may determine a valid customer identity in response to the receiving the identification credentials and the customer data The system may receive a request to generate a token from at least one of the issuer web app or the issuer native app, wherein the request comprises at least one of the identification credential or a parent transaction account, and wherein the request comprises at least one of a customer defined authorization control or a customer defined generation control.

Abstract: At least some embodiments are directed to a system that receives a profile values associated from new user profiles of a computer network or system. A machine learning system determines a set of existing profiles that share at least one common profile value with the new user profile. A second machine learning model determines a set of existing user entitlements associated with the set of existing profiles. The new user profile is processed by a natural language processing engine to determine a set of new user entitlements from the set of existing user entitlements. The system provides the new user with access to electronic resources of the computer network. The system tracks the new user computer network or system activities and updates the new user profile based on the set of new user entitlements and the new user activity on the computer network or system.

Abstract: At least some embodiments are directed to a prediction system of software objects. The prediction system predicts a first aspect of a user profile utilizing a categorization machine learning model and a user activity profile, the user profile and the user activity profile are associated with a user. The user activity profile comprises a plurality of values associated with demographics and historical activity data of the user. The prediction system predicts a software object associated with the user profile utilizing an optimization machine learning model, the first aspect of the user profile, and a second aspect of the user profile. The software object is optimized with respect to at least one competitive interest between the user associated with the user profile and an entity associated with the software object. The prediction system outputs the software object to a client computing device of the user.