Payment Means Recommendation System Using Future Consumption Pattern Prediction
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Solution Overview
Problem
Users face difficulty in utilizing payment means benefits due to the diversity of offers, as existing recommendation technologies only consider current consumption patterns and not future patterns, leading to unsuitable benefits and information asymmetry for vulnerable groups like the elderly.
Innovation Solution
A method and system that acquire payment information, analyze consumption patterns, specify user groups, predict future consumption patterns, and recommend personalized payment means by referring to group attributes, ensuring both current and future patterns are considered and addressing information asymmetry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only current consumption pattern is used to recommend payment means, then the recommendation system is simple and fast, but the accuracy of benefit suitability is low
Solution Approach 1:
The system performs preliminary actions by predicting future consumption patterns before making payment means recommendations. This involves analyzing historical payment information, identifying consumption patterns, forecasting future behavior, and pre-determining suitable payment means with benefits that will be relevant when the user actually uses them, rather than waiting until after consumption occurs
Solution Approach 2:
The recommendation system transitions from static current-pattern-based recommendations to dynamic future-pattern-based recommendations. The system continuously updates consumption pattern predictions based on new payment information and changing user behavior, making the recommendations adaptive and evolving over time to maintain high accuracy
2Adaptability or versatility
If diverse payment means benefits are offered, then user benefit options increase, but user ability to figure out suitable payment means decreases
Solution Approach 1:
The system uses feedback mechanisms by continuously monitoring user payment information and consumption patterns, analyzing the effectiveness of recommended payment means, and using this feedback to refine future predictions and recommendations. This creates a closed-loop system that learns from user behavior to improve recommendation accuracy while maintaining diversity of options
Solution Approach 2:
The recommendation system operates as a self-service mechanism that automatically analyzes user payment information, identifies consumption patterns, predicts future behavior, and generates personalized payment means recommendations without requiring active user effort. Users simply need to provide payment information, and the system handles the complex analysis and recommendation generation autonomously
3Measurement precision
If manual information input is required for payment recommendation, then information accuracy may be high, but information asymmetry for vulnerable groups increases
Solution Approach 1:
The system introduces an intermediary mechanism that automatically collects and processes payment information from various sources (payment gateways, transaction systems, financial institutions) without requiring direct user input. This intermediary layer bridges the information gap by gathering data systematically and processing it through pattern analysis algorithms, ensuring accurate recommendations even for users who cannot manually provide information
Data Source
AI summary
Provided is a method for recommending a payment means, including acquiring payment information of at least one payment means associated with a user, analyzing a consumption pattern of the user by referring to the acquired payment information, specifying a group associated with the user by referring to the consumption pattern, predicting a future consumption pattern of the user by referring to attributes of the specified group, and determining a payment means to be recommended to the user by referring to a result of the predicting.