Payment Instrument Selection Model
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Solution Overview
Problem
Conventional proxy card systems do not utilize user selections of payment instruments and related transaction details to predict and provide the preferred payment instrument for transactions, leading to suboptimal choices during purchases.
Innovation Solution
A computer-implemented method and system that associate multiple financial accounts with a user proxy account, establish a model based on user configuration data, identify the most suitable financial account for a transaction by comparing transaction data, allow user input for alternative selections, and modify the model for future transactions based on user preferences and transaction outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user manually selects a backing payment instrument at the time of purchase, then the user has control over the selection, but the process requires additional user action and time
Solution Approach 1:
The system performs preliminary actions by establishing a payment instrument model in advance based on user configuration data, transaction history, and preferences. This model is ready before the actual transaction occurs, enabling automatic identification of the most suitable payment instrument without requiring manual user input at the time of purchase.
Solution Approach 2:
The system incorporates feedback mechanisms where user selections and transaction outcomes are logged and used to continuously modify and improve the payment instrument model. This feedback loop enables the system to learn from user behavior and automatically refine its recommendations, balancing automation with user control.
2Productivity
If the system automatically selects a payment instrument without user input, then the process is faster and more efficient, but the system lacks accuracy in predicting user preferences
Solution Approach 1:
The system performs preliminary actions by establishing a payment instrument model in advance based on user configuration data, transaction history, and preferences. This model is ready before the actual transaction occurs, enabling automatic identification of the most suitable payment instrument without requiring manual user input at the time of purchase.
Solution Approach 2:
The system incorporates feedback mechanisms where user selections and transaction outcomes are logged and used to continuously modify and improve the payment instrument model. This feedback loop enables the system to learn from user behavior and automatically refine its recommendations, balancing automation with user control.
3Measurement precision
If the system collects and analyzes user transaction data and selections, then the accuracy of future predictions improves, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of payment instrument selection into distinct functional modules: a configuration module for collecting user preferences, a model establishment module for creating the payment instrument model, a transaction analysis module for comparing pending transactions with the model, and a model modification module for continuous improvement. This segmentation manages complexity while maintaining predictive accuracy.
Solution Approach 2:
The payment instrument model serves as an intermediary between raw user transaction data and the final payment instrument selection. This model abstracts and structures the data, making it easier to process and analyze without requiring direct complex data handling at each transaction point.
4Measurement precision
If the user provides configuration data and preferences in advance, then the system can make more accurate recommendations, but the initial setup process becomes more complex
Solution Approach 1:
The system segments the complex task of payment instrument selection into distinct functional modules: a configuration module for collecting user preferences, a model establishment module for creating the payment instrument model, a transaction analysis module for comparing pending transactions with the model, and a model modification module for continuous improvement. This segmentation manages complexity while maintaining predictive accuracy.
Data Source
AI summary
Selecting payment instruments for proxy card transactions comprises associating a plurality of financial accounts with a user proxy account; establishing a model for the user account based on configuration data received from a user, the model comprising identification of a particular financial account to use as a backing instrument for a transaction; receiving data associated with a pending transaction regarding the pending transaction; identifying a financial account to use as a backing instrument for the pending transaction based on a comparison of the model with the pending transaction data; receiving a selection of an alternate financial account to use instead of the identified financial account as the backing instrument; modifying the model account based on the selection of the alternate financial account and the data associated with the pending transaction; and utilizing the modified model in a subsequent selection of a financial account for a subsequent transaction.


