Dynamic Trust Score Calculation Using Entity-Specific Risk Weights
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Solution Overview
Problem
Trust is a complex and elusive measure in social and business interactions, as it varies based on different factors and changes over time, making it difficult to quantify and capture effectively.
Innovation Solution
A system and method for calculating trust scores that consider various factors such as verification data, network connectivity, ratings, demographics, location, and transactions, including crowdsourced information, to provide a comprehensive trustworthiness assessment for entities like users, organizations, and businesses, with adjustable weights based on specific contexts and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple factors and data sources are considered in trust score calculation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The trust score calculation system is segmented into multiple independent data sources (verification data, network connectivity, ratings data, crowdsource data, transaction history) that can be individually processed and then combined. This modular approach allows comprehensive assessment while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The system implements a universal trust score calculation framework that can accommodate multiple types of entities (users, organizations, businesses) and multiple data sources through a single multi-functional architecture. The weighted combination approach allows the same system to handle diverse trust assessment scenarios without requiring separate specialized systems for each case.
2Adaptability or versatility
If trust score calculation considers entity-specific preferences and contexts, then adaptability is improved, but calculation time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating component scores for each data source (verification, network connectivity, ratings, crowdsource, transaction history) and storing them. When a trust score is needed, these pre-computed components are quickly combined using weighted sums, significantly reducing calculation time while still allowing entity-specific weight adjustments for different contexts.
Solution Approach 2:
The system implements dynamic adaptability by allowing weights for different data sources to be adjusted based on entity-specific preferences and transaction contexts. The weighting factors can be modified in real-time to reflect changing trust requirements, enabling the system to adapt to different scenarios without complete recalculation of all components.
3Reliability
If crowdsource information is integrated into trust score calculation, then reliability is improved, but information processing complexity increases
Solution Approach 1:
The system uses an intermediary aggregation mechanism that collects crowdsource information from multiple users and entities, then processes it through a standardized weighting and combination function. This intermediary layer transforms raw, unstructured crowdsource data into a refined component score that can be reliably integrated with other trust indicators, reducing the burden of direct processing.
Solution Approach 2:
The system incorporates feedback loops where crowdsource information is continuously collected, processed, and used to update trust scores. The feedback mechanism allows the system to learn from aggregated crowd opinions and adjust weighting factors over time, improving reliability while the automated feedback processing reduces manual intervention complexity.
Data Source
AI summary
Systems and methods are described herein for learning an entity’s trust model and risk tolerance. An entity’s trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity’s trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity’s trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity’s trust score, as reported to a requesting entity, may be adjusted based on that requesting entity’s own trust model, or how “trusting” the requesting entity is.