Automated Image Anomaly Detection for Appraisal Reports
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Solution Overview
Problem
Current manual and non-computerized appraisal report review processes are inefficient, leading to high costs and time consumption, with a low probability of detecting fraudulent or erroneous reports, as they can only review a small percentage of submissions, allowing potential fraud and inaccuracies to go undetected.
Innovation Solution
An image anomaly detection system that automates the processing of appraisal reports by extracting images, generating image IDs, and analyzing image quality indices to detect duplicates, poor quality images, and modifications, using engines like duplicate detection, image quality assessment, and image modification detection engines to flag anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review processes are used, then human judgment can be applied, but processing time is excessive and only a small percentage of submissions can be reviewed
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computerized system that uses image processing algorithms and machine learning models to analyze appraisal report images, thereby eliminating human time constraints and enabling processing of 100% of submissions
Solution Approach 2:
The system enables self-service automation where the computerized review system independently performs image analysis, anomaly detection, and fraud identification without requiring human intervention for each submission, allowing continuous high-volume processing
2Reliability
If manual review processes are used, then reviewers can exercise judgment, but the probability of detecting fraud is low due to limited review capacity
Solution Approach 1:
The patent segments the review process into multiple specialized automated engines including image quality assessment, duplicate detection, and anomaly detection, each targeting specific fraud indicators to comprehensively analyze 100% of submissions with high detection accuracy
Solution Approach 2:
The system creates digital copies and analyzes image data through multiple detection engines simultaneously, enabling comprehensive fraud detection across all submissions without physical constraints on review capacity
3Speed
If automated image processing is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The complex automated system is divided into distinct functional modules including image extraction, quality assessment, duplicate detection, and anomaly detection engines, allowing each component to be optimized independently while maintaining overall processing speed
Solution Approach 2:
The patent implements a universal platform that handles multiple review functions (image quality checking, duplicate detection, fraud analysis) through a single integrated system, reducing overall complexity compared to multiple separate systems
Data Source
AI summary
Systems and methods for detecting image anomalies include extracting one or more detected images from a submission file received from at least one computing device and generating an image identification (ID) for each of the one or more images. One or more image quality indices are determined for the submission file based on at least one of predetermined image features, an image type of the one or more images, and submission file attributes, and one or more image anomalies associated with the one or more images of the submission file are detected based on at least one of the image ID and the one or more image quality indices.