Identity Element Risk Scoring for Real-Time Fraud Interaction Control
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Solution Overview
Problem
Conventional techniques for risk assessment in interactive computing environments focus on predicting fraudulent or malicious activity at an interaction level rather than at an identity element level, lacking comprehensive and granular analysis, and are inefficient in handling large-scale datasets without compromising computing performance.
Innovation Solution
Generating a risk indicator associated with individual identity elements by analyzing a binomial distribution of historical data, allowing for preemptive measures such as flagging or denying interactions with high risk indicators, and utilizing machine learning to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional interaction-level risk assessment techniques are used, then the system can operate with simpler processing, but the measurement precision and granularity of risk assessment deteriorates
Solution Approach 1:
The patent segments the risk assessment process by introducing identity element-level analysis alongside interaction-level assessment. Each identity element (email, phone, device ID) is independently evaluated using binomial distribution models, allowing granular risk measurement without requiring complete redesign of the overall assessment system.
Solution Approach 2:
The patent changes the assessment parameters from binary interaction outcomes to probabilistic identity element risk scores based on binomial distributions. This transformation enables more precise risk measurement by considering multiple historical interactions and fraud events, converting simple pass/fail assessments into nuanced probability-based evaluations.
2Measurement precision
If comprehensive historical data analysis is performed for each identity element, then the risk assessment accuracy improves, but the computing resources and processing time increase
Solution Approach 1:
The patent extracts only the essential elements needed for risk assessment: total interaction count and fraud event count for each identity element. By focusing on these two key parameters rather than analyzing complete interaction histories, the system achieves accurate binomial distribution modeling with minimal data extraction and processing.
Solution Approach 2:
The patent employs lightweight binomial distribution calculations that require minimal computational resources compared to complex machine learning models. Each identity element's risk indicator is computed independently using simple probabilistic formulas, enabling efficient processing that can be discarded and recalculated as new data arrives.
3Productivity
If real-time risk assessment is implemented for large-scale datasets, then the productivity and response time improve, but the device complexity and processing requirements worsen
Solution Approach 1:
The system implements self-updating risk indicators that automatically recalculate when new interaction data arrives. Each identity element's binomial distribution parameters are continuously updated with new interactions and fraud events, enabling real-time risk assessment without requiring centralized batch processing or complex coordination between system components.
Data Source
AI summary
A system can generate a risk assessment associated with an identity element used in interactions associated with a user entity. For example, the system can receive historical data related an identity element associated with a user entity, the identity element used in a set of interactions associated with the user entity. The system can generate a binomial distribution of the historical data associated with the identity element. The system can determine, based at least in part on the binomial distribution of the historical data, a risk indicator associated with the identity element. The system can control, based at least in part on the risk indicator associated with the identity element, an interaction involving a target entity and the user entity using the identity element.