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.

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.

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.

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.

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.

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.

The invention provides a compliance detecting method of payment applications in a virtualized environment, and pertains to the field of security technology of payment applications. The detecting method is used for compliance detection for PCI DSS isolation demands. The detecting method can determine whether it is a compliance state by analyzing the current virtual machine domain and its connection from data stream, and can also determine whether it is a compliance state by analyzing the purity of network flow of virtual machines. The detecting method is suitable for a virtualized environment and makes the detection of compliance accurate in the virtualized environment, thus being advantageous for guaranteeing the security of payment applications.

A telecommunications method for call forwarding including storing information regarding a called party in at least one local database, where the information regarding the called party is stored in a user account and making a telephone call by dialing a telephone number with a telephone, where the telephone is contained in a telephone management system. The method also includes determining whether the telephone number is associated with the user account and transferring, via the switchboard, the call to the called party if the telephone number is associated with the user account, where the telephone management system is in communication with a revenue management system, and also where the revenue management system contains at least one local database.

A POS payment terminal (1) using a mobile communication device (4), such as a mobile phone, is created over a temporary connection between the merchant's Sales Device (2) with the removable memory card (18), where the removable memory card (18) is inserted into the customer's slot of the mobile communication device (4). The Sales Device (2) contains a secure memory (6) with the POS terminal's identification data in the form of a SAM card or an ICC card (9) or directly in the form of a secure element on the circuit printed. The payment terminal (1) is created before or during the payment process over a temporary connection between a Sales Device (2) with a mobile communication device (4), which is held by the customer and which has a removable memory card that can have an independent communication element, especially of the NFC type. The payment cryptogram is of standard form, e.g. in the EMV format and it is sent online or offline in batches to the payment processing center (15) according to the amount paid and the preset risk management.

A telecommunications method for call forwarding including storing information regarding a called party in at least one local database, where the information regarding the called party is stored in a user account and making a telephone call by dialing a telephone number with a telephone, where the telephone is contained in a telephone management system. The method also includes determining whether the telephone number is associated with the user account and transferring, via the switchboard, the call to the called party if the telephone number is associated with the user account, where the telephone management system is in communication with a revenue management system, and also where the revenue management system contains at least one local database.