AI Vector Identity Resolution for Real-Time Verification and Fraud Detection
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Solution Overview
Problem
Existing identity verification and fraud detection systems are prone to errors due to fuzzy string matching, inability to process image data, and high resource consumption, leading to inaccurate and delayed determinations.
Innovation Solution
A system employing vectorization of identity components, including textual and image data, using machine learning and AI to enhance matching operations and reduce resource burden, utilizing a vector search database for real-time verification and detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fuzzy string matching is used for identity verification, then the system can handle variations in data input (nicknames, typos), but the matching accuracy deteriorates and errors increase
Solution Approach 1:
The patent replaces the mechanical string matching algorithm with a biometric recognition system that uses facial image analysis. Instead of comparing text strings character-by-character, the system extracts facial features and compares them using geometric and photometric transformations, eliminating the fundamental limitations of string matching while maintaining adaptability to input variations
Solution Approach 2:
The system transforms the identity verification problem from text-based parameter comparison to image-based parameter comparison. By changing the data type from strings to facial images and the comparison metric from string similarity to facial feature similarity, the system achieves both adaptability to variations and high matching accuracy
2Measurement precision
If image matching is incorporated into identity verification, then the verification accuracy improves, but the system complexity increases because string matching algorithms cannot process images
Solution Approach 1:
The patent replaces the inadequate string matching system with a dedicated image processing system that uses facial feature extraction and geometric transformation algorithms. This substitution enables the system to process image data effectively, achieving high verification accuracy while managing complexity through specialized image processing techniques rather than general-purpose string algorithms
Solution Approach 2:
The system creates a universal identity verification platform that can handle multiple data types (textual information and image data) through a unified approach. The facial recognition component works independently of string matching, allowing the system to process both traditional text-based identifiers and biometric image data within the same framework
3Device complexity
If traditional string matching systems are used, then the system is simple to implement, but computing resources are excessively consumed and processing speed decreases
Solution Approach 1:
The patent extracts only the essential identifying features from facial images (geometric relationships between facial landmarks and photometric characteristics) rather than processing entire images or comparing all pixels. This extraction approach dramatically reduces computational requirements while maintaining high verification accuracy, enabling real-time processing
Solution Approach 2:
The system creates simplified representations (copies) of facial images in the form of feature vectors and geometric models. Instead of storing and comparing full-resolution images, the system works with compressed feature representations that require minimal computational resources for comparison while preserving the essential identifying information
Data Source
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AI summary
In various embodiments of the present invention, identity components such as name, date of birth, address, social security number, driver's license number etc. are transformed into vector representations which are then stored in a vector search database. In preferred embodiments, each vectorized identity component represents a column in a table which is implemented as a database and which is incorporated into a trained model. This trained model is then used on a real time or near real time basis to make identity verification and fraud detection decisions in connection with proposed "transactions". These "transactions" can take various forms such as purchasing a good or service, opening an account, performing a background check and/or any other action wherein it is necessary or desirable to confirm that the person seeking to perform the transaction is who they say they are.