67 results about "Balance transfer" patented technology

Share of Wallet (“SoW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. In addition to credit card companies, SoW outputs may be useful to companies issuing, for example: private label cards, life insurance, on-line brokerages, mutual funds, car sales/leases, hospitals, and home equity lines of credit or loans. “Best customer” models can correlate SoW outputs with various customer groups. A SoW score focusing on a consumer's spending capacity can be used in the same manner as a credit bureau score.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The outputs include the size of the consumer's spending wallet over a particular time period, the total number of the consumer's revolving cards, the consumer's revolving balance, the consumer's average pay-down percentage for revolving cards, total number of the consumer's transacting cards, the consumer's transacting balance, a number of balance transfers transacted by the consumer, the total amount of the consumer's balance transfers, the consumer's maximum revolving balance, the consumer's maximum transacting balance, the consumer's credit limit, size of the consumer's revolving spending, and the size of the consumer's transacting spending.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The outputs can be used as attributes to consider in developing a credit bureau scorecard.

Share of Wallet (“SoW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. In addition to credit card companies, SoW outputs may be useful to companies issuing, for example: private label cards, life insurance, on-line brokerages, mutual funds, car sales/leases, hospitals, and home equity lines of credit or loans. “Best customer” models can correlate SoW outputs with various customer groups. A SoW score focusing on a consumer's spending capacity can be used in the same manner as a credit bureau score.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. “Best customer” models can correlate SOW outputs with various customer groups for targeted marketing.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The likelihood of default determined by the SOW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The likelihood of default determined by the SOW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide scores that describe a consumers spending capability, tradeline history including balance transfers, and balance information. Share of wallet scores can be used as a parameter for determining whether or not to accept and/or guarantee a check. The share of wallet can be used to calculate a risk value of a customer. For example, the scores can weight one or more factors related to the check writer and differentiate between a low-risk customer and a high-risk customer.

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. “Best customer” models can correlate SOW outputs with various customer groups for targeted marketing.

Systems and methods are disclosed for offering a credit card account to a potential home buyer. The systems and methods may include determining if a consumer is a potential home buyer and offering a credit card account to the consumer if the consumer is a potential home buyer. Next, the systems and methods may include transferring a balance to the credit card account equal to at least a portion of closing costs associated with the purchased home by the consumer. In addition, the systems and methods may include providing a discount to the consumer on items purchased by the consumer using the credit card account.

In some embodiments, exemplary user interfaces for provisioning an electronic device with an account are described. In some embodiments, exemplary user interfaces for providing usage information of an account are described. In some embodiments, exemplary user interfaces for providing visual feedback on a representation of an account are described. In some embodiments, exemplary user interfaces for managing the tracking of a category are described. In some embodiments, exemplary user interfaces for managing a transfer of items are described. In some embodiments, exemplary user interfaces for managing an authentication credential connected with an account are described. In some embodiments, exemplary user interfaces for activating a physical account object are described. In some embodiments, exemplary user interfaces for managing balance transfers are described.

Providing an account-based product. A communications portal is provided and consumer data is received via the portal. At least one derived characteristic, e.g., qualification, is determined contemporaneously from the consumer data. A product set is presented, via the portal, including at least one product determined at least in part by the derived characteristic. A selection from the product set is received via the portal, and at least one product feature, e.g., balance transfer, of at least one selected product is activated contemporaneous with the product selection.

A method and system for the issuance of Instant Credit has an Instant Credit engine that performs instantaneous analysis. The method and system can operate along side existing “batch” processes. In addition, the method and system offer various channels of distribution for the Instant Credit products. With the present invention, Instant Credit and other Instant services (e.g., instant account balance transfers, etc.) can be obtained via, Internet, point-of-sale and other distribution channels. The present system also enables highly dynamic product offerings, which can be generated “instantaneously” based on a variety of factors, such as, for example, the channels of distribution (e.g., telephone or Internet), the product sought and/or even the particular individual and/or entity that is applying for a line of credit.

This invention constitutes a method for taking a set of input parameters specifying a person or family's assets, liabilities, income and expenses and computing the optimal amount of cash to withdrawal from each credit source or asset (including liquidation of physical assets, credit card balance transfers, and use of loan sources) and the allocation of expenses and cash flow across assets and liabilities so as to maximize net worth by a particular date. The method consists of computing marginal and average expense and return functions for each asset and liability to do a no-withdrawal monthly optimization from the start month to the target month on which to maximize net worth, then recursively computing optimal withdrawal amounts for each source of capital on specific months, ultimately finding the optimal withdrawal amount for each source on each of these months and returning the optimal allocation of expenses and the resulting income and cash-flow across assets and liabilities. This invention is being applied to an online environment, where the user inputs the necessary details and optimization takes place on the server.

The invention relates to a calibration measurement device for steady-state thrust of a 4-25N attitude control engine in a thermal vacuum environment. The calibration measurement device comprises a base transfer frame, shock absorbing blocks, a fixed frame, a balance transfer plate, a single-component balance measurement device, a propellant pipeline, propellant pipeline fixing devices and an in-situ calibration device, wherein the shock absorbing blocks are uniformly mounted between the base transfer frame and the fixed frame; the propellant pipeline fixing devices are fixedly mounted on the periphery of the fixed frame; the propellant pipeline is mounted through the propellant pipeline fixing devices, one end of the propellant pipeline is communicated with an external propellant supply system, and the other end of the propellant pipeline is communicated with a propellant inlet of a to-be-measured attitude control engine; the single-component balance measurement device is mounted at one end of the fixed frame through the balance transfer plate, and the in-situ calibration device is mounted at the other end of the fixed frame. The calibration measurement device meets the requirements of the 4-25N attitude control engine for low thrust calibration measurement and solves the problems that high uncertainty is caused by pipeline constraint, high temperature and vibration in a measurement process, calibration before test run is not proper, the calibration precision is poor and the like.