Identity-Element Trust Scoring for Nuanced Access Control
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Solution Overview
Problem
Existing binary identity verification systems lack nuanced insights into personally identifiable information (PII) and trust factors, making them vulnerable to impersonation and failing to assess risk accurately.
Innovation Solution
A trust assessment system that generates a trust indicator by combining risk scores and affiliation scores based on PII elements, using machine-learning models to determine weights and assess the trustworthiness of a target entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary identity verification is used to control access, then the system is simple to operate, but it lacks nuanced insights into PII and trust factors, making it vulnerable to impersonation
Solution Approach 1:
The patent segments the identity verification process into multiple independent scoring components: affiliation scores for each PII element, risk scores for each element, and composite trust scores. Each component is calculated separately using machine learning models, allowing the system to assess different aspects of identity trustworthiness independently before combining them into an overall verification result.
Solution Approach 2:
The patent transforms the binary verification output into a multi-dimensional scoring system with continuous parameters. Instead of simple pass/fail results, the system generates affiliation scores, risk scores, and trust scores that capture nuanced levels of confidence and risk associated with each identity element, enabling more granular access control decisions.
2Loss of information
If binary verification output is used, then the system is easy to implement, but it does not provide insights into how the output was generated or what factors were considered
Solution Approach 1:
The patent implements feedback mechanisms that provide detailed information about the verification process. The system returns not only the final trust score but also breakdowns of affiliation scores and risk scores for each PII element, along with explanations of which factors contributed to the overall assessment. This feedback loop enables transparent and explainable AI decision-making.
Solution Approach 2:
The verification output is segmented into multiple informative components including element-level affiliation scores, element-level risk scores, and their combinations. This segmentation preserves and communicates the nuanced information about different aspects of identity verification, allowing stakeholders to understand which specific factors influenced the overall trust assessment.
3Reliability
If binary assessment is used, then the verification process is quick, but it does not capture a measure of risk associated with each element of an identity
Solution Approach 1:
The patent performs preliminary risk assessment by calculating affiliation scores and risk scores for each PII element before combining them into the final trust score. Machine learning models pre-process and evaluate individual identity elements, identifying risk factors early in the verification process, which allows for optimized decision-making and potential early termination for clearly fraudulent or clearly legitimate cases.
Solution Approach 2:
The verification system dynamically adjusts the depth and scope of analysis based on initial assessments. The multi-layered scoring approach allows the system to quickly evaluate obvious cases while performing more detailed analysis only when needed, balancing processing speed with comprehensive risk assessment through adaptive verification depth.
Data Source
AI summary
A system can generate a trust indicator associated with a target entity. For each data source, the system can: retrieve identity data associated with the target entity based on the identity of the target entity; generate a set of element risk scores and a set of affiliation scores associated with each element of the set of elements. The system can determine an aggregate element risk score and an aggregate element affiliation score. The system can determine a risk score by combining the aggregated element risk scores based on a first set of element weights and an affiliation score by combining the aggregated element affiliation scores based on a second set of weights. The system can transmit a responsive message including at least the trust indicator in which the trust indicator is based on the risk score and the affiliation score.