Predictive Tax Estimation Using Cohort Analysis
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Solution Overview
Problem
Current electronic tax return preparation systems do not provide estimated results to users before they begin preparing their tax returns, and they do not efficiently identify relevant taxpayer data sources, leading to inefficient data collection and inaccurate results.
Innovation Solution
A system using predictive models, such as Pearson product-moment correlation and cohort analysis, to generate predicted taxpayer data and identify relevant data sources, allowing users to calculate estimated results before starting the tax return process and directing resource allocation to more relevant data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If predictive models are used to generate predicted taxpayer data before preparation begins, then users can obtain estimated results earlier for better planning, but the system complexity increases due to implementing predictive modeling algorithms
Solution Approach 1:
The system performs preliminary actions by executing predictive models to generate predicted taxpayer data before the user actually begins preparing their tax return. This allows the estimated result to be calculated in advance using available data and predictive algorithms, giving users early insights for financial planning without waiting for complete data collection and manual preparation.
2Productivity
If the system identifies and prioritizes relevant data sources using predictive analysis, then data collection efficiency improves, but the computational resources and processing time increase
Solution Approach 1:
The system changes parameters by dynamically adjusting the priority and selection criteria of data sources based on predictive analysis. The predictive model evaluates various data sources and assigns priorities based on their relevance to the taxpayer's situation, allowing the system to focus computational resources on the most impactful data collection activities rather than uniformly processing all potential sources.
3Measurement precision
If the system calculates estimated results using predicted taxpayer data, then the accuracy of early estimates improves, but the reliability of the predictive model becomes critical and may introduce new sources of error
Solution Approach 1:
The system implements feedback mechanisms where the predictive model's outputs are continuously refined based on actual taxpayer data and preparation outcomes. User feedback, corrections, and actual tax return results are fed back into the system to improve the predictive algorithms, ensuring that the model learns from real-world data and becomes more reliable over time while maintaining improved accuracy.
Data Source
AI summary
A system for calculating an estimated result for an electronic tax return to be prepared before a user begins to prepare the electronic tax return using an electronic tax return preparation program includes a server computer having a predictive model, and a user computer having a browser program. The user computer and the browser program are operatively coupled to the server computer and the predictive model by a network. The server computer is configured to obtain a first taxpayer datum associated with a taxpayer and execute the predictive model, which generates a predicted taxpayer datum for the taxpayer based on the first taxpayer datum. The server computer is configured to calculate the estimated result using the predicted taxpayer datum. The user computer is configured to display the estimated result to the user before the user begins to prepare the electronic tax return using the electronic tax return preparation program.