A sales management system includes a registration device having a processor configured to perform commodity registration based on operation of a customer who purchases a commodity. The sales management system also includes a gate device having a processor configured to execute error processing in response to receiving, as an input, information indicating that payment for the commodity registered by the registration device is completed is not input when the customer passes the gate device. The processor of the registration device is also configured to receive stop information indicating that the customer stops shopping without purchasing the commodity output information for passing the gate device. The processor of the gate device is also configured to receive, as the input, the information for passing the gate device, and allow passage of the customer without executing the error processing.

The disclosure herein relates to AI-based methods and systems of using machine-learning to identify deceptive merchants in payment transactions such as recurring payment transactions. For example, the AI-based systems and methods may train and use an aggregate merchant matcher based on entity matching to identify merchant identifiers and/or acquirers that may be used by a merchant, train and use transaction classifiers to classify transactions as deceptive, recognize merchants based on an N-density aware transaction embedding learned from transaction data, and train and use a merchant classifier to classify merchants as deceptive.

A method for facilitating expansion of primary payment instruments comprises: determining a type of a primary payment instrument; determining, from a set of primary key generation rules, a primary key generation rule corresponding to the type of the primary payment instrument, the primary key generation rule specifying at least one primary payment instrument element for generating a primary payment key, each primary key generation rule from the set of primary key generation rules corresponding to a different type of primary payment instrument and specifying at least one primary payment instrument element for generating a primary payment key, each primary payment instrument including a set of primary payment instrument elements; generating, according to the primary key generation rule, a primary payment key for the primary payment instrument; and storing a corresponding relationship between the primary payment instrument and the primary payment key.

A fraud detection as a service system is provided that can be implemented via one or more microservices that can be instantiated on an operation support system and/or business support systems. Each fraud detection microservice instantiated can have one or more fraud detection models and sets of rules for a particular type of incoming data stream or transaction and/or interaction data. In this way, there can be more than one fraud detection microservice operating on the OSS/BSS allowing each fraud detection microservice to be dynamically updated in real time, provide continuous integration and continuous delivery of services, and work with a particular data flow, providing for an optimal fraud detection process.

Various examples are directed to systems and methods for detecting fraud. A financial institution system may receive, from a transaction device, a transaction authorization request describing a transaction to be drawn on an account of a user. The financial institution system may also receive, from the user computing device, environmental data describing an environment of the user and determine that the environmental data indicates an anomalous condition. Based at least in part on the determining, the financial institution system may decline the transaction.

A user profile scoring platform may analyze a transaction log of a transaction account of a user to determine, based on transactions of the transaction log, a qualification status of the user, wherein the qualification status indicates that a characteristic of the user satisfies a threshold qualification metric. The user profile scoring platform may determine, based on the qualification status, a transaction-based score associated with the user, wherein the transaction-based score is determined using a transaction log analysis model. The user profile scoring platform may obtain, based on receiving the access information, a user score associated with a user transaction history that is associated with a plurality of transaction accounts that are associated with the user and different from the transaction account. The user profile scoring platform may perform an action based on the transaction-based score and the user score.

Systems and methods of the present disclosure include computer systems for improving data security. To do so, an authorization request associated with a user profile is received, including a time-stamp for a profile activity, a profile identifier, and a value. Application analytics data for a software application on a user device associated with the user profile is accessed, including an open event indicator indicating a time of a loading, and a close event indicator indicating a time of a termination. An open period of the software application at the time-stamp is determined based on the application open event indicator and the application close event indicator. A reduced set of fraud checks is selected and executed when the time-stamp associated with the profile activity is within the open period of the software application. A fraud determination is generated, and an authorization notification is generated based on the fraud determination.

According to certain embodiments, a system comprises a memory and a processor operably coupled to the memory. The memory is operable to store historical data associated with previous data processing transactions, the historical data comprising a set of attributes, including an entity name attribute. The processor is configured to obtain the historical data and classify each previous data processing transaction as a member of a group of unauthorized previous data processing transactions or as a member of a group of authorized previous data processing transactions. The processor is further configured to determine a first pattern associated with the entity name attributes used by the members of the group of unauthorized previous data processing transactions, and to facilitate identifying a new data processing transaction as unauthorized based at least in part on an entity name attribute associated with the new data processing transaction using the first pattern.

Disclosed are systems, methods, and non-transitory computer-readable media for determining compliance of a transaction using machine learning. A transaction compliance system automatically determines whether a requested transaction is in compliance with a set of compliance rules associated with the requested transaction. The set of compliance rules may be regional rules and regulations established by a governmental body and/or rules defined by a merchant. The transaction compliance system accesses the set of compliance rules corresponding to the transaction and determines whether the requested transaction is in compliance with the set of compliance rules. The requested transaction may be completed or denied based on the resulting output.

Methods and systems for providing configuration management using customized request headers. One system includes an electronic processor configured to receive, from a requesting device, a request including a customized request header, the customized request header including a custom configuration parameter. The electronic processor is also configured to perform a fraud analysis function based on the request. The electronic processor is also configured to generate a response including a customized response body based on the customized request header. The electronic processor is also configured to transmit the response to the requesting device, where the requesting device performs functionality based on information included in the customized response body.