Address Scoring System for Identity Verification and Risk Assessment
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Solution Overview
Problem
Existing systems lack efficient methods to dynamically identify patterns in delivery points and generate scores for authentication, risk assessment, and volume shift analysis, which are crucial for secure and optimized delivery operations.
Innovation Solution
The implementation of a method that receives items for delivery, extracts information about associated entities and addresses, and uses this data to calculate confidence scores for identity verification, risk scores for behavioral analysis, and volume shift values for delivery optimization, employing machine learning models and probabilistic modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models and probabilistic modeling are applied to generate confidence scores, risk scores, and volume shift values, then the accuracy and reliability of delivery operations is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The scoring system is divided into separate modular components: confidence score generation, risk score generation, and volume shift value calculation. Each module processes specific data types and applies targeted algorithms, making the overall complex system more manageable and easier to maintain while improving reliability through specialized processing.
Solution Approach 2:
The patent introduces an intermediary data processing layer that aggregates and pre-processes delivery data before it reaches the machine learning models. This intermediary layer filters, cleans, and structures raw delivery information, reducing the computational burden on the scoring models and enabling more accurate predictions without proportionally increasing system complexity.
2Speed
If real-time data processing and pattern identification are implemented, then the speed of delivery decisions is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and pattern identification during off-peak hours or in batch mode, pre-calculating confidence scores and risk assessments for known delivery patterns. This allows real-time operations to rely on pre-computed results, reducing on-demand processing time while maintaining high decision speed.
Solution Approach 2:
The scoring system dynamically adjusts its processing intensity based on the complexity of the delivery scenario. For routine deliveries with established patterns, the system uses simplified lookup tables and pre-computed scores. For complex or unusual delivery situations, the system automatically activates full machine learning analysis, optimizing the balance between processing speed and accuracy.
Data Source
AI summary
In one aspect, a method of confirming identity of an entity is disclosed. The method comprises receiving a plurality of items for delivery to an address, obtaining, from the items, information regarding an entity associated with the items and the address, and delivering the items to the address. The method may also comprise identifying an expected identity of the entity, receiving a request to confirm an identity of the entity using third-party identity verification via a user interface, and determining, based on the information regarding the entity, a confidence score for the expected identity. The method may further comprise determining whether the confidence score is greater than or equal to the threshold value and generating a response to the request. The method may additionally comprises displaying the response via the user interface.