Dynamic Trust Score System for Merchant Transaction Risk Assessment
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Solution Overview
Problem
Existing computer systems face challenges in collecting and providing distributed consumer data to merchants in a usable format, making it difficult for merchants to assess the trustworthiness of consumers for transactions.
Innovation Solution
A system that calculates a dynamic trust score by combining transaction data, third-party data, and user data, including reputational data and historical transaction data, and transmits this score to merchants for authorization purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed consumer data is collected from multiple computers and networks, then the completeness and accuracy of consumer assessment is improved, but the complexity of data collection and integration increases
Solution Approach 1:
The patent introduces a centralized data aggregation system that acts as an intermediary between multiple distributed data sources and merchants. This system collects, standardizes, and integrates consumer data from various computers and networks, transforming fragmented distributed data into a unified format that merchants can easily access and use for accurate consumer assessment without directly managing complex data collection infrastructure.
Solution Approach 2:
The patent combines multiple distributed data sources into a single integrated data repository. By merging data from various computers and networks into a centralized system, the patent achieves comprehensive consumer data collection while simplifying the complexity through unified data management and standardized formats that can be easily accessed by merchant systems.
2Reliability
If consumer data is collected and provided to merchants, then transaction security is improved, but the loss of time for data processing and transmission increases
Solution Approach 1:
The patent implements preliminary data processing by pre-collecting, validating, and organizing consumer data in the centralized system before merchants need it. This advance preparation ensures that when merchants request consumer data, it is already processed and ready for immediate use, thereby maintaining high transaction security while minimizing data processing time during actual transactions.
Solution Approach 2:
The patent creates standardized data copies that can be rapidly transmitted to multiple merchants simultaneously. By generating pre-processed data copies in the centralized system, the patent enables fast data distribution to merchants without requiring time-consuming real-time processing for each transaction, thus maintaining security while reducing time loss.
3Measurement precision
If a comprehensive trust score system is implemented, then the ability to identify beneficial consumers is improved, but the device complexity for calculating and managing trust scores increases
Solution Approach 1:
The patent creates a universal trust score system that serves multiple functions: it assesses consumer trustworthiness, identifies beneficial consumers, provides risk assessment, and supports transaction authorization. This multi-functional system consolidates various assessment capabilities into a single integrated trust score mechanism, improving comprehensive consumer evaluation while reducing the need for multiple separate complex systems.
Solution Approach 2:
The patent transforms complex multi-dimensional consumer data into a simplified numerical trust score parameter. By converting comprehensive consumer information (payment history, creditworthiness, behavioral patterns) into a single quantifiable trust score, the patent maintains high assessment precision while significantly simplifying the complexity of trust evaluation and merchant decision-making processes.
Data Source
AI summary
Systems and methods for the calculation of a dynamic trust score are disclosed. The dynamic trust score may indicate a likelihood that the consumer will complete the transaction in a positive manner. The system may calculate the dynamic trust score based on various static and dynamic variables including digital identity data, internal data, third-party data, private data, and/or data from the transaction initiated by the consumer.