Machine Learning Model for Financial Report Adjustment
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Solution Overview
Problem
Manual review of financial reports across multiple jurisdictions is time-consuming and prone to errors, especially when dealing with thousands of fields and values, which can lead to compliance violations due to inaccuracies.
Innovation Solution
A machine learning model that self-learns from historical adjustments to automatically recommend corrections to values within reports, providing a user interface for accountants to review and implement suggested changes before submission, with features like trend analysis and interactive elements for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of financial reports is performed, then accuracy can be maintained through human verification, but time consumption increases significantly when dealing with thousands of fields across multiple jurisdictions
Solution Approach 1:
The system performs self-learning by automatically analyzing historical adjustments and identifying patterns without human intervention. The machine learning model trains itself on past data to predict accurate values in current reports, enabling the system to serve itself in improving accuracy while reducing manual time investment.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated machine learning system. The ML model processes thousands of fields across multiple jurisdictions automatically, substituting human manual verification with computational algorithms that can handle large volumes of data efficiently and consistently.
2Reliability
If multiple people/services are used to provide documentation for multiple jurisdictions, then compliance accuracy can be maintained, but device complexity and operational overhead increase
Solution Approach 1:
The machine learning model serves as a universal system that handles compliance requirements across multiple jurisdictions simultaneously. Instead of requiring separate processes for each jurisdiction, the single ML model adapts to different reporting requirements, tax laws, and formats, reducing operational overhead while maintaining compliance accuracy.
Solution Approach 2:
The patent merges multiple compliance processes into a single integrated system. The machine learning model consolidates what would otherwise require multiple people or services by combining pattern recognition, data validation, and adjustment recommendation capabilities into one unified automated platform that handles all jurisdictions.
3Productivity
If automated processing is implemented without machine learning, then time consumption is reduced, but error detection capability decreases compared to manual review
Solution Approach 1:
The system performs preliminary learning by training on historical adjustment data before actual report processing. This preliminary action enables the ML model to pre-establish patterns and relationships, allowing it to quickly and accurately detect errors in new reports without requiring manual review, thus maintaining both speed and precision.
Solution Approach 2:
The system incorporates feedback mechanisms where historical adjustments and corrections are continuously fed back into the training data. This feedback loop allows the ML model to learn from past errors and improve its detection capabilities over time, ensuring that automated processing maintains high error detection accuracy while preserving processing speed.
Data Source
AI summary
Provided are a system and method that use machine learning to recommend adjustments to digital documents that are included within a report managed by a software application. The system may also provide a trend analysis as part of the recommendation. In one example, the method may include detecting a request to open a digital document from a user via a user interface, populating the user interface with content from the digital document and executing a machine learning model on values within the digital document to identify a value among the values that is to be adjusted to a different value based on previous adjustments by the user to previous reports, activating a user interface element associated with the identified value within the user interface, and in response to a selection of the user interface element, instantiating a display of the different value on the user interface.