Predictive Model for Tax Return Error Detection
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Solution Overview
Problem
Current tax return preparation systems lack effective methods for identifying and correcting errors in electronic tax returns, particularly in predicting potential errors and ensuring compliance with tax authority requirements, which can lead to inaccuracies and penalties.
Innovation Solution
The implementation of predictive models, such as logistic regression, naive bayes, K-means clustering, and neural networks, in conjunction with declarative constraint-based error checking, to verify tax data and generate alerts for potential errors, allowing users to review and correct data before submission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error checking methods are used in tax return preparation, then the system is simple and easy to operate, but the accuracy and reliability of error detection is insufficient
Solution Approach 1:
The patent replaces traditional mechanical error checking methods with machine learning-based predictive models. These models use historical tax return data and patterns to predict potential errors, substituting rule-based systems with intelligent algorithms that can identify complex error patterns beyond simple validation rules.
Solution Approach 2:
The patent introduces an intermediary layer between data entry and final submission that uses predictive models to analyze and flag potential errors. This intermediary system processes tax return data through multiple predictive algorithms that assess risk and likelihood of errors before the return is finalized, adding a layer of intelligent verification.
2Measurement precision
If predictive models are executed multiple times with different input data sets, then the verification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent executes predictive models at multiple stages during tax return preparation, performing preliminary error detection before final submission. By running predictions incrementally as data is entered rather than all at once, the system identifies errors early when they are easier to correct, reducing the need for repeated full-execution cycles.
Solution Approach 2:
The patent applies predictive models selectively to high-risk fields and data points rather than uniformly across all fields. By focusing computational resources on areas with higher error probability based on historical patterns, the system achieves thorough verification where needed while reducing unnecessary processing in low-risk areas.
Data Source
AI summary
Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives first and second tax data and populates respective fields of the electronic tax return. The system executes a predictive model such as logistic regression, naive bayes, K-means clustering, clustering, k-nearest neighbor, and neural networks. First tax data is an input into the predictive model, which generates an output, which is compared with second tax data. An alert is generated when the second tax data does not satisfy pre-determined criteria relative to the first output generated by the predictive model. The same or other predictive model may be used as additional tax data is received for subsequent tax data analysis.