Payment Card Binding via Multi-Dimensional Trust Evaluation
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Solution Overview
Problem
The existing methods for binding a new payment card to a digital wallet, such as 3-D Secure and Micro Charge, are complex and result in a high payment failure rate, with failure rates exceeding 30%-40% due to their complexity.
Innovation Solution
A payment card binding method that involves receiving a binding request, determining trust levels based on account, device, and environment data, and using a trained classifier to set a new card trust level and payment limit, simplifying the binding process and reducing payment risks by initiating a payment request to validate the card and adjust trust parameters dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional verification methods (3-D Secure or Micro Charge) are used to bind a payment card, then payment security is improved, but the binding process complexity increases and payment failure rate exceeds 30%-40%
Solution Approach 1:
The patent changes the verification parameters from traditional authentication methods to a trust evaluation system that assesses account trust level, device trust level, and environment trust level. This parameter transformation simplifies the binding process while maintaining security by dynamically evaluating multiple trust dimensions rather than relying on complex sequential verification steps.
Solution Approach 2:
The patent introduces a trained classifier as an intermediary between the trust level assessments and the final binding decision. This classifier processes the account, device, and environment trust levels to determine the new card trust level (NCTL) and payment limit restriction, acting as a mediator that simplifies the overall verification process while maintaining comprehensive security evaluation.
2Reliability
If traditional verification methods (3-D Secure or Micro Charge) are used to bind a payment card, then payment security is improved, but the payment failure rate increases to more than 30%-40%
Solution Approach 1:
The patent performs preliminary trust evaluations of the account, device, and environment before finalizing the card binding. By assessing trust levels in advance and determining the new card trust level through the trained classifier, the system prepares appropriate payment limit restrictions beforehand, reducing failures during actual payment transactions.
Solution Approach 2:
The patent implements dynamic trust level assessment where the account trust level, device trust level, and environment trust level are evaluated based on current data. The trained classifier dynamically determines the new card trust level and payment limit restriction, allowing the system to adapt to different risk scenarios and reduce unnecessary payment failures.
3Productivity
If a simple binding process is implemented, then the binding success rate is improved, but payment risk increases
Solution Approach 1:
The patent adds multiple evaluation dimensions by assessing account trust level, device trust level, and environment trust level separately. This multi-dimensional approach allows the system to maintain a simple binding process while comprehensively evaluating payment risk through the trained classifier that processes all three dimensions to determine the final new card trust level.
Solution Approach 2:
The patent implements feedback mechanisms where the trained classifier continuously evaluates the trust levels and adjusts the new card trust level and payment limit restriction based on the assessment results. This feedback loop ensures that payment risk is controlled while maintaining high binding success rates by providing appropriate payment limits based on trust evaluations.
Data Source
AI summary
A payment card binding method, a trust evaluation method, an apparatus, and an electronic device are provided. The payment card binding method includes: receiving a payment card binding request; sending a payment request to a payment system; in response to the payment request being successfully processed by the payment system, determining (1) an account trust level, (2) a device trust level, and (3) an environment trust level; inputting the account trust level, the device trust level, and the environment trust level into a trained classifier to determine a new card trust level (NCTL); determining a payment limit restriction for the digital wallet account using the payment card on the computing device; and binding, based on the NCTL and the payment limit restriction, the digital wallet account with the payment card for the digital wallet account to use the payment card for future payments.