Machine Learning Model for Dynamic Sales Transaction Compliance Validation
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Solution Overview
Problem
Companies face challenges in ensuring compliance with complex governmental standards for sales and purchase transaction data, leading to administrative costs and penalties due to errors in tax payments, especially in countries with intricate tax regulations like Brazil.
Innovation Solution
A machine learning model is applied to dynamically validate sales and purchase transaction data by analyzing patterns from previous data to identify errors and generate compliance standards, reducing the need for hard-coded rules and alerting users of potential noncompliance before submission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules are used to validate transaction data, then compliance validation can be performed, but the system becomes cumbersome and inflexible when standards change
Solution Approach 1:
The patent transforms the static rule-based validation system into a dynamic machine learning model that automatically adapts to changing compliance standards. The ML model learns from historical transaction data and corrective actions, continuously improving its validation capabilities without requiring manual rule updates when standards change.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically learn and update compliance validation rules from historical data patterns. The model autonomously identifies errors and generates corrective transactions without requiring manual intervention to update static rules when compliance standards evolve.
2Adaptability or versatility
If machine learning models are implemented to dynamically validate transactions, then adaptability to compliance changes improves, but computational resources and data processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on historical transaction data and corrective patterns before actual validation occurs. This upfront training reduces the computational burden during real-time validation, as the model has already learned the compliance patterns and can make rapid predictions on new transactions.
Solution Approach 2:
The patent applies partial action by implementing validation only where needed - the ML model focuses on identifying specific error patterns in transaction data rather than processing every single data point exhaustively. This selective validation approach reduces overall computational resource consumption while maintaining effective compliance checking.
3Reliability
If corrective transactions are created to compensate for errors, then compliance issues are addressed, but administrative costs and data footprint increase
Solution Approach 1:
The machine learning model applies preliminary anti-action by proactively identifying and flagging potential compliance errors in transactions before they are submitted. By detecting patterns that indicate future corrective actions would be needed, the system prevents errors from occurring in the first place, thereby avoiding the creation of additional corrective transactions and reducing data footprint growth.
Data Source
AI summary
Systems and methods for applying machine learning to dynamically validate a sales transaction document created by a user in a computing system are provided. Data comprising the sales transaction document is received. A machine learning model is applied to the sales transaction document to verify that the sales transaction document meets at least one compliance standard. The user is alerted if the sales transaction document does not meet the at least one compliance standard. The maching learning model is generated by: receiving first sales transaction data from a database; determining patterns based on the first sales transaction data, wherein the patterns indicate that corrective data was created to compensate for at least one error in original data, the at least one error indicating that the original data did not meet at least one compliance standard; and generating the machine learning model based on the determined patterns.