Aspects of the subject disclosure may include, for example, monitoring a first activity undertaken by a communication device during a first communication session, generating, based on the monitoring, first data that indicates an amount of time that is spent on the first activity, comparing, based on the generating, the first data to a threshold, and identifying, based on at least the comparing, an action to take when the amount of time that is spent on the first activity exceeds the threshold. Other embodiments are disclosed.

Embodiments provide methods and systems for upgrading an authorization request message in a dual message system format to an upgraded authorization request message in a single message system format without requiring any modifications to existing systems of acquirers and issuers. A transaction processing network computer may upgrade an authorization request message based on a score assigned to the transaction using a machine learning algorithm. The score indicates a likelihood that a final value of the transaction when finalized is same as an initial value of the transaction. If the score is above predetermined threshold, the transaction processing network computer upgrades the authorization request message to a single message system format.

Aspects of this disclosure relate to use of a monitoring platform for detection of money mule accounts. The monitoring platform may monitor financial and non-financial transactions and/or other activities associated with an account to generate various statistical and technology adaptation metrics. The statistical and technology adaptation metrics may be used by a rules engine to determine whether the account is a potential money mule.

A method, system, and computer programming product for checking that clusters representative of transactional activity of a group of persons exhibits sufficient variability including: receiving transactional data; forming clusters from the received transactional data representing groups of persons that behave similarly; determining that a cluster representing a group of persons that behave similarly is not sufficiently variable; and increasing, in response to the cluster representing the group of persons behaving similarly not being sufficiently variable, the variability of the cluster. Further including, in an embodiment, creating a superset cluster consisting of both the cluster and the parent of the cluster; creating test data using the superset as a baseline; injecting the test data into the superset cluster; determining if the superset cluster rejects the injected test data as an indication of insufficient variability.

The present disclosure relates generally to electronic transactions, and more specifically, to perform inter wallet transactions. A directory server may act as a network between a plurality of wallets. In some non-limiting embodiments or aspects, the directory server receives a request message from a first wallet server for initiating a transaction from a first wallet user to a second wallet user associated with a second wallet server. In some non-limiting embodiments or aspects, the directory server sends one or more details among a plurality of details associated with the second wallet server and the second wallet user to the first wallet server in response to the request message.

A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.

The present disclosure relates to a computer-implemented method for authenticating a transaction over a secure network. The method comprises receiving, by a first authentication server, a sensitive data payload and a cryptogram, wherein the first authentication server is configured to either receive or generate a token associated with the sensitive data payload; transmitting, by the first authentication server, the token and the cryptogram to a second authentication server, wherein the second authentication server is configured to validate the token and the cryptogram and generate a first message including a validation result; transmitting, by the second authentication server, the first message to an issuer server to authenticate the transaction; and reviewing, by the issuer server, the validation result and generating an authentication value including a validation flag based on the review of the validation result.

Systems and methods related to resource distribution for a fleet of machines are disclosed. A system may include a fleet of machines each having an associated resource capacity and a resource requirement to perform a task. The system may further include a controller having a resource requirement circuit to determine an aggregated amount of the resource requirement and an aggregated amount of the resource capacity. A resource distribution circuit may adaptively improve, in response to an aggregated amount of the resource capacity, an aggregated resource delivery of the resource.

Systems and methods for data augmentation in a neural network system includes performing a first training process, using a first training dataset on a neural network system including an autoencoder including an encoder and a decoder to generate a trained autoencoder. A trained encoder is configured to receive a first plurality of input data in an N-dimensional data space and generate a first plurality of latent variables in an M-dimensional latent space, wherein M is an integer less than N. A sampling process is performed on the first plurality of latent variables to generate a first plurality of latent variable samples. A trained decoder is used to generate a second training dataset using the first plurality of latent variable samples. The second training dataset is used to train a first classifier including a first classifier neural network model to generate a trained classifier for providing transaction classification.

Techniques are disclosed in which a computer system generates a transaction network graph from an initial set of transactions including known labels and attributes. The computer system may generate first and second matrices using first and second graph embedding routines from a training set of transactions that includes a first subset of transactions in the network graph. The first routine is based on anomalies in related transactions occurring at nodes in the transaction network graph that are multiple hops away while the second routine is based on anomalies in neighborhoods of similar transactions. In some embodiments, the computer system generates a final embedded matrix from the first and second matrices and uses the final matrix and a testing set of transactions that includes a second subset of transactions in the graph to train a machine learning model, where the trained model usable to determine whether unlabeled transactions are anomalous.