Dynamic Payment Option Recommendation System
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Solution Overview
Problem
Users face difficulties in determining the optimal combination of payment options that maximize savings or rewards during transactions, as they struggle to remember associated offers and incentives from various payment methods.
Innovation Solution
A method and system that receive billing information, obtain merchant rules and user preferences, compute rewards for each payment option, and provide a recommended payment option based on these factors, utilizing a server with processor-executable instructions to analyze and recommend the most rewarding payment choice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If users manually track and compute rewards from multiple payment options, then they can maximize savings, but the complexity and time required increases significantly
Solution Approach 1:
The system performs automatic reward computation and recommendation without requiring user intervention. The server autonomously calculates rewards from multiple payment options, compares them, and presents the optimal choice to the user, eliminating manual tracking effort while maintaining savings maximization
Solution Approach 2:
An intermediary server system is introduced between the user and the payment options. This server acts as a mediator that receives billing information, computes rewards from various payment methods, and recommends the optimal payment option, thereby reducing user cognitive load and transaction complexity
2Measurement precision
If the system computes rewards for multiple payment options using multiple parameters, then the recommendation accuracy improves, but the computational complexity increases
Solution Approach 1:
The system pre-fetches and stores offer information, payment option details, and user preferences before the actual transaction occurs. This preliminary preparation allows the reward computation to be performed quickly and accurately when billing information is received, without excessive computational complexity during the transaction moment
Solution Approach 2:
The reward computation process is segmented into distinct components: retrieving billing information, obtaining offer details, fetching user preferences, calculating rewards for each payment option, and generating recommendations. This segmentation allows each component to be optimized independently and processed in a structured manner
Data Source
AI summary
A method of providing a personalized payment option for a user includes (a) receiving billing information from a merchant server associated with a merchant, (b) obtaining one or more rules associated with the merchant, (c) obtaining one or more offers associated with one or more payment options associated with the user and a plurality of preferences associated with the user, (d) computing a reward associated with each of the one or more payment options based on the one or more offers, the billing information, the plurality of preferences, and the one or more rules, and (e) providing at least one recommended payment option among the one or more payment options based on the computed reward associated with each of the one or more payment options.