Propensity Model for Reducing Electronic Submission Rejections
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Solution Overview
Problem
Electronic submissions to pass/fail interfaces often get rejected due to failure to meet specifications, incurring costs and counting against quotas, despite potential for rejection being predictable.
Innovation Solution
A propensity model-based optimization system that uses machine learning and artificial intelligence to analyze submissions, predict acceptance likelihood, and suggest corrective actions to users before submission, thereby reducing the chances of rejection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electronic submissions are made to pass/fail interfaces without prior validation, then submission speed is maintained, but rejection rate increases causing cost loss and quota consumption
Solution Approach 1:
The system performs preliminary validation of electronic submissions against pass/fail interface specifications before actual submission. A propensity model analyzes submission data to predict acceptance likelihood and identifies required corrections in advance, allowing users to fix issues before submitting to the actual interface, thereby avoiding rejection costs and quota consumption.
2Reliability
If propensity model analysis is performed on all submissions, then acceptance rate improves, but processing time and computational resources increase
Solution Approach 1:
The system dynamically adjusts the depth and scope of propensity model analysis based on submission characteristics, confidence thresholds, and historical data. For high-confidence submissions or those with clear compliance, minimal analysis is performed, while ambiguous cases receive more thorough scrutiny, optimizing the balance between acceptance rate improvement and processing time.
Data Source
AI summary
Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. An apparatus comprises a processor and a memory that stores code executable by the processor to receive an electronic submission for a pass/fail interface, identify information from the electronic submission to suggest to a user for entering into an input field for the pass/fail interface prior to submitting the electronic submission to the pass/fail interface to reduce a likelihood that the electronic submission will be rejected at the pass/fail interface, determine the likelihood that the electronic submission will be accepted by the pass/fail interface, and submit the electronic submission to the pass/fail interface in response to the likelihood satisfying a threshold.