A workstation is disclosed for internet transactions between non-business users substantially consisting of a series of desktop apparatuses and of a series of movable apparatuses that interact with one another to permit the operation of the workstation itself and the desktop apparatuses feature: computers, screens, printers, scanners, mouse devices, keyboards, cameras, whereas the movable apparatuses feature: smartphones, tablets, notebooks, ultrabooks and the connection and interaction method between the different apparatuses is by internet connection or WIFI between the desktop apparatuses and 3g, 4g, 4.5g, 5g, LTE, WIFI connection between the movable apparatuses. The workstation provides different apparatuses that interact between one another in response to the action of at least two parties who are a buyer and a seller and a third party who is a courier for transporting the item that is the object of the sale. The workstation in question is provided with an operating procedure for performing a sale transaction concerning an item, a corresponding payment and delivery of the item that comprises a series of operating steps including: sending a request, opening a procedure, sending money and a photograph, selecting the method of shipment and delivery and selection of a third party with the function of courier and closure of the transaction with the item being delivered to the buyer and the money being transferred to the seller.

A method including: receiving one or more datasets indicating call activity corresponding to a phone number; analyzing the one or more datasets to identify unusual call activity; and generating a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud.

An example method performed by a processing system obtaining a first port-in number for a first mobile device from a first mobile communications service provider, wherein the first port-in number is known to be involved in fraudulent activity, constructing a social graph of communications between the first port-in number and a plurality of other numbers associated with a plurality of other communications devices, identifying, by the processing system, a maximal subgraph of the social graph, wherein the maximal subgraph connects the first port-in number and a subset of the plurality of other numbers that includes those of the plurality of other numbers for which a usage metric is below a predefined threshold for a defined period of time prior to the first port-in number being ported into the first mobile communications service provider, and identifying, by the processing system, a potential fraud ring, based on the maximal subgraph.

Systems and methods for identification of a controlled-environment facility resident in possession of a contraband communications device capture or otherwise accept managed access data and/or contraband communications device assessment data for contraband communications devices operating in the controlled-environment facility. Controlled-environment facility resident call data for each resident of the controlled-environment facility is gathered from the controlled-environment facility resident communications system. Correlations in the managed access data and/or assessment data with the controlled-environment facility resident communications system call data are analyzed to identify each resident of the controlled-environment facility in possession of a contraband communications device.

A system and method for resolving an unsatisfactory quality of experience (QoE) for an application executed on a wireless device that accesses a CSP network is described. A QoE network appliance generates an integrated event stream that includes a RAN data set, a CN data set, a NWDAF data set, a QoE latency measurement, a QoE bandwidth measurement, and a QoE packet loss rate measurement. A measured QoE score is generated with the RAN data set, the CN data set, and the NWDAF data set. The measured QoE score is associated with a QoE latency measurement, a QoE bandwidth measurement, and a QoE packet loss rate measurement. A robotics process automation (RPA) module receives the integrated event stream when the measured QoE score fails to satisfy the QoE requirement. The RPA module performs one or more automated actions to improve the measured QoE based on information from the integrated event stream.

Methods and systems are presented for generating a machine learning model using enhanced gradient boosting techniques. The machine learning model is configured to receive inputs corresponding to a set of features and to produce an output based on the inputs. The machine learning model includes multiple layers, wherein each layer includes multiple models. To generate the machine learning model, multiple models are built and trained in parallel for each layer of the machine learning model. The multiple models use different subsets of features to produce corresponding output values. After a layer in built and trained, a collective error may be determined for the layer based on the output values from the different models in the layer. An additional layer of models may be added to the machine learning model to reduce the collective error of a previous layer.

In one embodiment, this invention analyzes demographic data that is associated with a specific street address when presented as an address change on an existing account or an address included on a new account application when that address is different from the reference address (e.g., a credit bureau type header data). The old or reference address and the new address, the new account application address or fulfillment address demographic attributes are gathered, analyzed, compared for divergence and scaled to reflect the relative fraud risk.

Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user’s normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.

Aspects of the disclosure relate to recognizing and authenticating mobile devices based on unique cross-channel bindings. In some embodiments, a computing platform may receive, from a telephone agent support computer system, call information associated with a telephone call. Subsequently, the computing platform may identify a source device that placed the telephone call, based on binding information maintained by the computing platform for the source device. Based on identifying the source device that placed the telephone call, the computing platform may load user information associated with a user account linked to the source device. Next, the computing platform may set one or more authentication flags for the user account based on the binding information. Then, the computing platform may send, to the telephone agent support computer system, the user information and authentication information based on the one or more authentication flags set for the user account linked to the source device.

Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.