Payment Channel Recommendation Using Historical Behavior Prediction
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Solution Overview
Problem
Existing payment channel recommendation systems fail to accurately recommend payment channels with high success rates that users are willing to select, leading to inefficiencies and unsatisfactory user experiences during transactions.
Innovation Solution
A method that determines a recommendation score for payment channels based on historical data, including user and channel performance features, to predict user behavior and recommend channels with high success rates, using a mapping relationship trained on payment features and result features such as success rates and user willingness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple payment channels are provided for user selection, then payment flexibility and user choice are improved, but payment efficiency and user experience deteriorate due to selection complexity and time consumption
Solution Approach 1:
The system automatically performs payment channel selection based on historical data and user behavior patterns, eliminating the need for users to manually evaluate and select from multiple payment channels. The recommendation system serves itself by learning from past payment outcomes and autonomously determining optimal channel recommendations.
Solution Approach 2:
The system incorporates feedback loops that continuously learn from payment results, user selections, and behavioral data to refine recommendation accuracy. By analyzing whether recommended channels were selected and whether payments succeeded, the system iteratively improves its recommendation algorithm to better balance flexibility with efficiency.
2Device complexity
If payment channel recommendation is provided without accurate prediction, then implementation simplicity is maintained, but payment success rate and user satisfaction worsen due to inaccurate recommendations
Solution Approach 1:
The system performs preliminary analysis of historical payment data, user behavior patterns, and channel performance metrics before making recommendations. By pre-processing and storing relevant features in advance, the system builds predictive models that can quickly recommend reliable payment channels without adding significant real-time complexity.
Solution Approach 2:
The system replaces manual analysis and simple rule-based recommendations with data-driven machine learning models. These models automatically process historical data and user behavior patterns to predict payment success rates, substituting complex analytical mechanics with trained algorithms that improve reliability without proportionally increasing implementation complexity.
3Reliability
If historical payment data is analyzed in detail to improve recommendation accuracy, then payment success rate is improved, but data processing time and computational resources worsen
Solution Approach 1:
The system extracts only the most relevant features from historical payment data, such as user payment patterns, channel performance metrics, and transaction characteristics. By selecting and extracting only critical data elements rather than processing complete historical records, the system maintains high recommendation accuracy while significantly reducing data processing time and computational overhead.
Solution Approach 2:
The system processes a partial set of historical data that is sufficient for accurate recommendations rather than analyzing complete historical records. By identifying and processing only the essential portion of data needed for reliable predictions, the system achieves good recommendation accuracy without the excessive time and computational resources required for comprehensive data analysis.
Data Source
AI summary
A computer-implemented method includes: determining a target user corresponding to a payment request and a plurality of payment channels corresponding to the payment request; obtaining payment features based on the target user and the plurality of payment channels, in which the payment features include features representing historical characteristics of one or more payments made by the target user using one or more of the plurality of payment channels; determining, based on the payment features, a recommendation score corresponding to each of the plurality of payment channels in accordance with a prediction model; and generating, based on the determined recommendation scores of the plurality of payment channels, a recommendation including at least one of: a selection of at least one of the plurality of payment channels, and a manner of presentation of at least one of the plurality of payment channels.