Tax Delinquency Resolution System Using Deep Learning
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Solution Overview
Problem
Current tax delinquency systems, such as those provided by the IRS, lack the ability to calculate and present optimal repayment plans based on a taxpayer's current earnings and allowable expenses, leading to inadequate repayment structures and potential missed savings opportunities, as they do not account for individual disposable income and do not provide comprehensive options for resolving tax delinquencies.
Innovation Solution
A system and method that takes a taxpayer's financial information, computes available repayment options using data from tax agencies, and presents these options along with necessary documentation, utilizing a deep learning algorithm and inference engine to determine the most optimal repayment plan, including information on lien or levy release programs, and provides a user-friendly interface for negotiation with tax authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current IRS repayment systems are used, then repayment plans are simple to implement (fixed 72-month division), but they do not account for taxpayer disposable income and allowable expenses, resulting in inadequate repayment structures
Solution Approach 1:
The system applies local quality by customizing repayment plans to each taxpayer's specific financial situation, allowing different repayment terms and amounts based on individual disposable income and allowable expenses rather than applying a uniform approach to all taxpayers
Solution Approach 2:
The system implements dynamics by making repayment plans adaptable and flexible, allowing the repayment amount and duration to be adjusted based on the taxpayer's current earnings and expenses, rather than using a fixed static structure
2Measurement precision
If comprehensive tax data analysis is performed (144,000 data points), then optimal repayment options can be identified, but the complexity of the system increases significantly
Solution Approach 1:
The system uses an intermediary approach by employing trained deep learning algorithms and inference engines that act as mediators between the complex tax data and the taxpayer, automatically processing the 144,000 data points and presenting simplified optimal repayment options without requiring the taxpayer to directly navigate the complexity
Solution Approach 2:
The system implements self-service by enabling taxpayers to independently access and analyze their own repayment options through automated tools that process their financial information and generate personalized repayment plans, eliminating the need for expensive professional tax services
3Loss of information
If taxpayers hire professional tax services to analyze repayment alternatives, then comprehensive analysis can be obtained, but significant expenses are incurred
Solution Approach 1:
The system enables taxpayers to perform comprehensive repayment analysis themselves through automated deep learning algorithms and inference engines that process their financial data and generate optimal repayment options, eliminating the need to hire expensive professional tax services while maintaining complete analysis capability
Data Source
AI summary
Systems and methods for simultaneously presenting to a user with a plurality of available options for resolving delinquent debts to local, state and national tax authorities and providing the user with appropriate documentation to resolve his/her delinquency status based on a selected option. The system uses all available information from the tax agency website and personal information of the taxpayer, such as expenses and income, to compute the most favorable resolution solution for the taxpayer. The system further uses deep learning to ranks the generated options based on previously accepted solutions by the tax agency.