Systems and methods for fraud monitoring are disclosed, including: receiving a transaction request associated with a first instrument of a user; extracting, characteristics of the transaction request; identifying, by the first processor, user data based on the transaction request; determining a fraud severity value and notification value based on inputting the characteristics and user data into a fraud machine learning model; performing a first fraud action based on the fraud severity value; wherein the first fraud action is at least one selected from the group of locking the first instrument for a period of time, deactivating the first instrument, and electronically transmitting a first query message to a user device associated with the first instrument; and transmitting a fraud notification based on the notification value, wherein the fraud notification includes severity information associated with the fraud severity value.

A computing system for authenticating users utilizing an interactive chatbot is provided. The computing system includes a processor in communication with a memory, and the processor programmed to: (i) receive an authorization request message for a transaction initiated by a user, wherein the authorization request message includes transaction data, (ii) retrieve user data associated with the user, (iii) determine, based upon the transaction data and the user data, a risk associated with the transaction, (iv) generate, based upon the risk associated with the transaction, one or more prompts for the user, (v) transmit, via the interactive chatbot, the one or more prompts to the user, (vi) receive user input in response to the one or more prompts, and (vii) embed an authentication indicator into the authorization request message, wherein the authentication indicator indicates whether the user is authenticated based upon the user input.

A device may verify the authorization of the payee by a payee identification server. A device may create a record in a database on the payee identification server, the record including, either directly or indirectly, payee identification information, payee address, payee phone number, payee tax information, one or more methods of payment accepted by the payee comprising a type of payment, institution information, and account information. A device may verify said record with one or more verification sources. A device may record the results of the verification in the record. A device may create a d-token to point to the record. A device may send the d-token to the payee. A device may receive, by the payee identification server, the d-token from a third party. A device may retrieve the one or more of the methods of the payment accepted by the payee.

Systems and methods for supporting regulatory requirements for the distribution of controlled and non-controlled items such as, for example, non-controlled prescriptions (Rx), medical devices, and controlled substances in countries such as the United States and Canada, are provided. The systems and methods incorporate a license verification module that is configured to perform license validation for a particular order placed for a controlled and/or non-controlled item. In certain embodiments, the license verification module compares order data to historically sorted data and if one or more discrepancies exist, validation is unsuccessful. The license verification module may further query a third party database for updated license information upon validation failure. The systems and methods further incorporate a suspicious order monitoring module that is configured to perform a plurality of checks on the order to identify the order as an “order of interest” that may be further investigated and deemed to be suspicious.

Generally, embodiments of the invention are directed to methods, computer readable medium, servers and systems for enabling merchants to use hosted order pages (HOPs) and/or silent order posts (SOPs) and thereby avoid handling payment information and the costs associated Payment Card Industry (PCI) compliance, while at the same time utilize third-party fraud detection screens and thereby avoid costs associated with fraudulent transactions.

Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

In an online marketplace, buyers and sellers engage in transactions. Some transactions are fraudulent. The online marketplace uses a fraud detection system that identifies fraudulent transactions before an account holder is defrauded. In some example embodiments of the systems and methods described herein, a fraud detection system uses information that is indirectly associated with a transaction to determine if the transaction is fraudulent. A user interface is provided that shows relationships between accounts and objects. In response to input received via the user interface, the fraud detection system performs an action on an account, an object, or a transaction.

Various methods, apparatuses, and media for implementing a check fraud detection module are provided. A processor parses received digital image of a check into separate portions, one of the portions including a signature of an account holder. The processor applies a machine learning model to generate a new 128-dimensional embedding of the signature of the account holder parsed from the received digital image of the check and compares it preauthorized historical reference 128-dimensional embedding of the signature stored onto a database. The processor generates, based on comparing, a similarity score between the new 128-dimensional embedding of the signature and the preauthorized historical reference 128-dimensional embedding of the signature; and identifies whether the received check is fraudulent or not based on the generated similarity score.

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.

Data stream based event sequence anomaly detection for mobility customer fraud analysis is presented herein. A system obtains a sequence of events comprising respective modalities of communication that correspond to a subscriber identity associated with a communication service—the sequence of events having occurred within a defined period. Based on defined classifiers representing respective fraudulent sequences of events, the system determines, via a group of machine learning models corresponding to respective machine learning processes, whether the sequence of events satisfies a defined condition with respect to likelihood of representing a fraudulent sequence of events of the respective fraudulent sequences of events. In response to the sequence of events being determined to satisfy the defined condition, the system sends, via a user interface of the system, a notification indicating that the sequence of events has been determined to represent the fraudulent sequence of events.