Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

In an e-commerce risk screening system (900), transaction information is applied to multiple fraud risk models (915, 916, 918, 920) that produce raw scores, which are transformed with sigmoidal transform functions (910) to produce optimized likelihood of fraud risk estimates. Such estimates are combined using fusion proportions, producing a single point risk estimate, which is transformed with a sigmoidal function (910) to produce an optimized single point risk estimate.

Method and apparatus for evaluating fraud risk in an electronic commerce transaction

The Customer Activated Multi-Value (CAM) card provides a customer with a primary use as a transaction card which can be further activated to have a secondary use as a credit card. The CAM card can be activated as a transaction card or as a transaction card with a credit card feature. Activation of the CAM card as a transaction card allows the customer to purchase goods and services from one specific vendor and to establish a relationship with that vendor for the goods or services rendered (for example, telephone calling card or gasoline product purchasing card) while enjoying reward benefits based on those to purchases. Activation of the credit feature of the card allows the customer to interact with any merchant that accepts traditional credit cards such as VISA®, MASTERCARD®, AMERICAN EXPRESS®, or DISCOVER®. The combination of features allows a customer to purchase various goods and services from several different merchants. If the customer chooses to activate the CAM card as both a transaction card and a credit card, the customer is given the benefit of using the card in both manners described above.

Customer activated multi-value (CAM) card

A selectable, multi-purpose card comprising a plurality of features stored in memory means operatively mounted on the card and selection means for allowing a user to select a feature in a few simple steps, preferably in a single step. In one embodiment the card includes a plurality of magnetic strips positioned on the card in a manner to allow swiping each magnetic strip separately using conventional reading devices. Each magnetic strip activates a different feature of the card. In another embodiment the card includes a programmable magnetic strip, a plurality of features stored in memory means mounted on the card, a plurality of buttons or contacts, and means for programming the magnetic strip with a different card feature. The card may also include a thin, flexible display.

Selectable multi-purpose card

The authors provide a critical examination of marketing analytics methods by tracing their historical development, examining their applications to structured and unstructured data generated within or external to a firm, and reviewing their potential to support marketing decisions. The authors identify directions for new analytical research methods, addressing (1) analytics for optimizing marketing-mix spending in a data-rich environment, (2) analytics for personalization, and (3) analytics in the context of customers’ privacy and data security. They review the implications for organizations that intend to implement big data analytics. Finally, turning to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.

Marketing Analytics for Data-Rich Environments

A computer-implemented method for predicting financial risk, which includes receiving first transaction data pertaining to transactions performed on a first financial account. The first financial account represents a financial account issued to a given account holder by a first account issuer. The method further includes receiving second transaction data pertaining to transaction performed on a second financial account different from the first financial account. The second financial account represents a financial account issued to the given account holder by a second account issuer different from the first account issuer. There is further included scoring the first transaction data and the second transaction data based on a preexisting model to form a score for the account holder. Additionally, there is included transmitting, if the score is below a predefined financial risk threshold, the score to one of the first account issuer and the second account issuer.

Financial risk prediction systems and methods therefor

Transaction scoring is performed in a distributed manner across a client-server computing system. A computing system for processing a transaction includes a server system and a client system. The server system is arranged to process information associated with the transaction, while the client system communicates with the server system and includes a key engine which is arranged to generate keys. The client system and the server system are arranged to cooperate to assess risk associated with the transaction. The client is arranged to send the keys generated by the key engine as a transaction to the server system.

Distributed quantum encrypted pattern generation and scoring

In one aspect, a system includes a transaction handler to process transactions, a data warehouse to store transaction data recording the transactions processed at the transaction handler, a profile generator to generate a profile of a user based on the transaction data, an advertisement selector to identify an advertisement based on the profile of the user in response to the transaction handler processing a transaction of the user, and a portal coupled to the transaction handler to provide the advertisement for presentation to the user in connection with information about the transaction of the user. In one example, the profile includes a plurality of values representing aggregated spending of the user in various areas to summarize the transactions of the user.

Systems and methods to deliver targeted advertisements to audience

Methods and apparatus for transforming scoreable transaction data into financial data features are disclosed. In one aspect, a computer-implemented method transforms transaction data (104) into a financial data feature (114) for assessing credit risks. The financial data feature (114) is extracted from the transaction data (104). The method involves obtaining the transaction data from a data source, and performing a set of operations (202) on the transaction data to transform the transaction data into the financial data feature. The set of operations (202) is selected only from a predefined set of classes of operations which are interrelated by a predefined order of precedence (402). Each operation in the set of operations is performed in an order based on the predefined order of precedence of a class associated with each operator.

Method and apparatus for pattern generation

In one aspect, a computing apparatus is configured to generate audience measurement data regarding presentation of information to a plurality of customers via one or more media channels, identify the plurality of customers to a transaction handler to request information, receive the information generated based on transaction data related to a plurality of transactions processed at the transaction handler, and generate a report based on the audience measurement data and the information generated based on the transaction data. The one or more media channels may include an online media channel, a mobile communication channel, or an offline media channel, such as television broadcast, radio broadcast, newspaper, magazine, direct mail, billboard, etc.

Kevin P. Siegel

Papers

Financial risk prediction systems and methods therefor

Distributed quantum encrypted pattern generation and scoring

Systems and methods to deliver targeted advertisements to audience

Method and apparatus for pattern generation

Systems and Methods for Panel Enhancement with Transaction Data