Probabilistic Tax Refund Range Estimation System
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Solution Overview
Problem
Traditional tax return preparation systems provide unstable and inaccurate estimated tax refund estimates, leading to user confusion and loss of faith in the system, as they rely solely on user-provided data without leveraging historical data for probabilistic inferences.
Innovation Solution
Combining rules-based tax preparation techniques with probabilistic inferences to generate an estimated tax refund range by analyzing current user data and historical tax data, even at early stages of the tax return preparation interview, using a system that gathers and processes tax rules data and historical user data to make accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tax return preparation systems provide estimated tax refund based solely on user-provided data, then the system can provide real-time estimates during the interview, but the estimates become unstable and inaccurate when data is incomplete
Solution Approach 1:
The system performs preliminary actions by gathering historical tax data and user profile information before the tax return preparation interview begins. This pre-collected data serves as a foundation for making accurate probabilistic inferences even when user-provided data is incomplete during the interview process.
Solution Approach 2:
The system introduces historical tax data and probabilistic inference algorithms as intermediaries between the incomplete user-provided data and the estimated tax refund calculation. These intermediaries fill in the gaps and stabilize the estimates by comparing against patterns from historical data.
2Measurement precision
If the system waits for complete user data before providing tax refund estimates, then the estimates would be more accurate, but the user experience deteriorates due to delays and loss of confidence
Solution Approach 1:
The system performs preliminary actions by pre-gathering historical tax data and user profile information before the interview. During the interview, it immediately applies probabilistic inferences to provide time-sensitive estimates while data is being collected, rather than waiting for completeness.
Solution Approach 2:
The system implements dynamic estimate updating throughout the interview process. The estimated tax refund evolves from initial probabilistic predictions to increasingly accurate calculations as more user data is provided, allowing the system to adapt its precision in real-time.
3Device complexity
If traditional systems provide a single point estimate for tax refund, then the calculation is simple, but the estimate undergoes large swings that confuse and discourage users
Solution Approach 1:
The system changes the parameter representation from a single point estimate to a probabilistic range or distribution. This transformation maintains computational simplicity while providing stability, as the range naturally accommodates uncertainty and prevents dramatic swings by showing confidence intervals rather than precise values.
Data Source
AI summary
A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.