Calibrated Risk Scoring and Sampling for Tax Return Review
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Solution Overview
Problem
Existing systems for reviewing tax returns struggle to accurately identify errors and adapt to seasonal fluctuations in tax return complexity, leading to inefficiencies in error detection and review processes.
Innovation Solution
A computing system employing calibrated risk scoring and sampling using a machine learning model to predict error probabilities, map records to risk buckets, and selectively sample returns for review, with continuous updates to risk bucket calibrations to account for seasonal changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional error review methods are used, then all tax returns are reviewed manually, but this results in high review time and low efficiency
Solution Approach 1:
The patent segments the tax return review process by dividing all returns into different risk buckets based on error probability scores. High-risk returns are separated for intensive review while low-risk returns are processed differently, eliminating the need to review all returns manually and significantly improving review efficiency
Solution Approach 2:
The system uses machine learning models to automatically assess error probabilities and categorize returns into risk buckets without human intervention. This self-service classification enables the system to prioritize reviews automatically, reducing manual review time while maintaining high productivity
2Adaptability or versatility
If fixed sampling rates are used, then the review process is simple, but it cannot adapt to seasonal fluctuations in tax return complexity
Solution Approach 1:
The patent implements dynamic sampling rates that automatically adjust based on the error probability distribution across different risk buckets. The system continuously monitors tax return characteristics and modifies sampling thresholds in response to seasonal fluctuations, enabling adaptability without requiring complex manual reconfiguration
Solution Approach 2:
The system incorporates feedback mechanisms where review outcomes and error patterns are fed back into the machine learning models. This feedback loop allows the system to learn from seasonal variations and automatically adjust risk bucket calibrations, achieving adaptability through data-driven adjustments rather than complex predetermined rules
3Measurement precision
If risk scoring is used to prioritize reviews, then high-risk returns are identified accurately, but this increases computational complexity
Solution Approach 1:
The patent applies different levels of analysis to different risk buckets. High-risk returns receive detailed scrutiny with higher sampling rates, while low-risk returns are processed with simpler methods. This local quality approach maintains high error detection accuracy for critical cases while reducing overall computational complexity by avoiding intensive analysis of all returns
Solution Approach 2:
The system uses machine learning models that continuously refine risk scoring parameters based on observed error patterns. By dynamically adjusting scoring parameters rather than using fixed complex rules, the system achieves accurate error detection with optimized computational requirements that adapt to actual data characteristics
Data Source
AI summary
A method implements calibrated risk scoring and sampling. Features are extracted from a record. A risk score, associated with the record, is generated from the features using a machine learning model. The record is mapped to a risk bucket using the risk score. The risk bucket may include multiple risk bucket records. The record is selected from the risk bucket records with a sampling threshold corresponding to the risk bucket. A form prepopulated with values from the record is presented to a client device.