Anonymized Image Encoding for Insurance Fraud Duplication Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Insurance companies face challenges in accurately and cost-effectively identifying fraudulent claims, particularly in the assessment of vehicle damage, due to the resource-intensive and error-prone manual methods currently employed.
Innovation Solution
An intelligent system using artificial intelligence-based software applications generates encodings of digital images, compares these encodings with a database of anonymized images, determines similarity metrics, and identifies potential duplication based on thresholds to detect fraudulent claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of claim histories and private investigators are used to identify fraudulent claims, then detection accuracy may be improved, but cost and resource consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis methods (private investigators, manual claim history analysis) with an automated digital image recognition system using artificial intelligence and machine learning algorithms. The system automatically compares uploaded images against a database of anonymized images to detect duplications, eliminating the need for expensive human investigation while maintaining high detection accuracy through sophisticated image encoding and comparison algorithms.
2Reliability
If more resources are allocated to manual fraud investigation, then detection capability improves, but processing speed and efficiency decrease
Solution Approach 1:
The system implements self-service automation where the AI-based image comparison system autonomously performs fraud detection without requiring human investigator intervention. The system automatically uploads images, generates encodings, compares against the database, and identifies fraudulent claims, enabling high-volume processing at scale while maintaining consistent detection capability across all claims.
Solution Approach 2:
The patent transforms the fraud detection process from a manual, time-consuming investigation to an automated parameter-based comparison system. By converting images into numerical encodings and comparing specific parameters (similarity metrics, threshold values), the system achieves rapid processing of numerous claims simultaneously while maintaining detection accuracy through configurable sensitivity parameters.
3Loss of information
If traditional manual methods are used for claim assessment, then detailed analysis can be performed, but error rates increase and consistency decreases
Solution Approach 1:
The system replaces error-prone manual analysis with automated AI-based image comparison that consistently applies the same detection algorithms to all claims. The machine learning models process images with uniform criteria, eliminating human errors and inconsistencies while maintaining the ability to detect subtle duplications through sophisticated feature extraction and encoding techniques.
Data Source
AI summary
Systems and methods for determining digital image duplication of a digital image with respect to anonymized digital images using an artificial intelligence based software application are implemented. Execution of machine readable instructions by a the processor causes the system to receive an upload of the digital image, generate an encoding of the digital image, compare the encoding of the digital image with a first stored encoding corresponding to a first anonymized digital image, determine a similarity metric, compare the similarity metric to a threshold, determine that the digital image is sufficiently duplicative of the first anonymized digital image associated with the first stored encoding when the first similarity metric equals or exceeds the threshold, and transmit a message indicating an identification of the digital image as sufficiently duplicative of the first anonymized digital image.