Machine-Learning Document Recognition for Remote Deposit Verification
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Solution Overview
Problem
Existing remote deposit capture systems face challenges in verifying the authenticity of checks and users due to environmental and hardware disparities, difficulty in validating document security features, and concerns about digital manipulation of image data.
Innovation Solution
A computing system that utilizes a machine-learning architecture to process video feeds from client devices, detecting documents, extracting information, and validating authenticity through object recognition engines, bounding boxes, and generating dynamic alignment indicators, while assessing risk scores and prompting users for additional verification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote deposit capture is implemented to eliminate travel and staffing requirements, then convenience and time efficiency are improved, but the ability to verify document authenticity and user identity deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the check from different angles and distances before submission. This includes capturing images of security features, microprint, and holograms in advance, allowing verification to occur before the deposit is finalized, thus maintaining both convenience and reliability
Solution Approach 2:
The system introduces an intermediary verification process that uses machine learning models to analyze captured images and detect authenticity features. This intermediary layer between the user and the deposit system enables remote verification of check authenticity without requiring physical inspection, resolving the contradiction between remote convenience and verification reliability
2Measurement precision
If detailed instructions are provided to users for remote deposit capture, then verification accuracy may improve, but user convenience and ease of operation deteriorates
Solution Approach 1:
The system implements self-service by enabling users to capture images without following detailed instructions. The machine learning models automatically analyze the captured images, detect security features, and verify authenticity, allowing users to simply capture and submit images without needing to know verification procedures, thus maintaining both accuracy and convenience
Solution Approach 2:
The system provides feedback to users about the quality and authenticity of their captured images in real-time. The machine learning models analyze images as they are captured and provide guidance only when necessary, allowing users to maintain convenience while achieving verification accuracy through automated feedback loops
3Reliability
If multiple security features are validated to ensure document authenticity, then reliability of verification improves, but device complexity and processing requirements worsen
Solution Approach 1:
The system segments the verification process into multiple independent machine learning models, each specializing in detecting specific security features such as microprint, holograms, and watermarks. This segmentation allows complex verification to be performed through multiple simple, specialized models rather than one complex model, reducing overall system complexity while maintaining high validation accuracy
Solution Approach 2:
The system employs universal machine learning models that can detect multiple different security features using the same underlying architecture. These multi-functional models can identify various authentication elements (microprint, holograms, watermarks) without requiring separate dedicated systems for each feature, thus improving reliability while controlling device complexity
4Adaptability or versatility
If electronic facsimiles are accepted for remote deposit, then accessibility and convenience improve, but susceptibility to digital manipulation and fraud worsens
Solution Approach 1:
The system replaces physical inspection mechanisms with electronic machine learning-based verification. Machine learning models analyze electronic facsimiles to detect security features and identify digital manipulation, enabling remote verification of image authenticity without requiring physical presence, thus maintaining accessibility while mitigating fraud risks through automated electronic verification
Data Source
AI summary
Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.