Trusted-Device Payments With Dynamic Transaction Limits
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Solution Overview
Problem
Traditional transaction limits in financial systems are static and do not adapt to the security level of transactions between user and merchant devices, leading to limitations in secure, high-value transactions.
Innovation Solution
A system that dynamically adjusts transaction limits in real-time based on a trust score calculated from transaction history and device interactions, using machine learning to evaluate the security and trustworthiness of transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static transaction limits are used, then security is maintained at a baseline level, but transaction flexibility and ability to accommodate high-value trusted transactions is limited
Solution Approach 1:
The patent applies dynamics by transitioning from static transaction limits to dynamic, real-time adjustable limits. The system continuously monitors trust scores between users and merchants, automatically adjusting transaction limits based on current trust levels. This allows the system to adapt transaction limits to the specific risk profile of each transaction pair, enabling higher limits for trusted relationships while maintaining security for less trusted ones.
Solution Approach 2:
The patent changes the parameter of transaction limits from fixed values to variable parameters that fluctuate based on trust scores. By making transaction limits a dynamic parameter rather than a static constraint, the system can optimize both security and flexibility. The limit parameter is continuously updated based on monitored trust metrics, allowing the system to respond to changing relationship dynamics between users and merchants.
2Reliability
If dynamic transaction limits are implemented, then transaction flexibility and security optimization are improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically monitoring trust scores and adjusting transaction limits without requiring manual intervention. The trust score calculation and limit adjustment processes are autonomous, with the system making its own decisions based on predefined criteria and real-time data. This reduces the operational burden on users and merchants while maintaining optimized security settings.
Solution Approach 2:
The patent employs feedback mechanisms where transaction outcomes and trust metrics continuously feed back into the system to refine future limit decisions. The system learns from actual transaction behavior patterns and adjusts trust scores accordingly, creating a self-improving feedback loop. This feedback mechanism allows the system to optimize security without requiring complex manual configuration, as the feedback automatically tunes the system parameters.
Data Source
AI summary
Systems and techniques may be used for transaction limit management based on trust relationships in financial transactions. An example technique may include receiving data obtained by a user device interacting with a merchant device, such as scanning a QR code or using near-field communication (NFC). The example technique may include processing the data to identify the user device within a financial transaction system linked to a merchant point-of-sale (POS) system. The example technique may further include analyzing the transaction history between the user device and the merchant device to establish a trust level and determine the trustworthiness of the user device, wherein an increase in a transaction limit may be approved upon establishing a sufficient trust level.