Financial Data Verification Automation for Consistent Audit Review
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Solution Overview
Problem
Conventional financial data processing systems rely heavily on the expertise of finance professionals, leading to varying report quality and insufficient identification of aberrant data, with complex and time-consuming processes for preparing financial reports and audits.
Innovation Solution
A system utilizing machine learning models and natural language processing to convert, parse, map, and verify financial data, applying business logic to automate data processing and highlight inaccuracies, thereby simplifying the financial report analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems rely on human expertise to process financial data, then flexibility and adaptability are maintained, but variability in report quality and inefficiency occur
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated computer-based system that uses machine learning models, natural language processing, and named entity recognition to process financial data. This substitution eliminates human variability while maintaining processing capability, directly resolving the contradiction between report quality consistency and processing efficiency.
Solution Approach 2:
The system enables self-service by automatically identifying areas requiring verification and flagging aberrant data without human intervention. The automated marking and highlighting functions allow the system to serve itself in detecting and prioritizing review needs, improving both consistency and efficiency simultaneously.
2Measurement precision
If manual review processes are used to verify financial data, then detailed analysis can be performed, but significant time and resources are consumed
Solution Approach 1:
The system performs preliminary actions by automatically processing and analyzing financial data before human review. It pre-identifies areas requiring verification and pre-flagges aberrant data, so that when humans do review, they are reviewing only the most critical items. This preliminary automated processing maintains verification accuracy while dramatically reducing the time required.
Solution Approach 2:
The system applies partial action by focusing verification efforts only on specific high-risk areas identified through automated analysis, rather than reviewing all data manually. This selective approach maintains measurement precision for critical items while reducing overall review time through intelligent prioritization.
3Manufacturing precision
If comprehensive financial data processing is performed manually, then thorough analysis is achieved, but labor-intensive processes and resource expenditure increase
Solution Approach 1:
The patent segments the comprehensive data processing task into distinct automated functions: machine learning model application, natural language processing, named entity recognition, data mapping, marking, and highlighting. This segmentation allows thorough processing to be achieved through coordinated automated subsystems rather than manual effort, maintaining precision while reducing the complexity of human resource requirements.
Solution Approach 2:
The system implements multi-functionality by creating a unified automated platform that performs multiple processing functions (analysis, verification, flagging, marking, highlighting) through integrated machine learning models and NLP capabilities. This universal system replaces multiple manual processes with a single sophisticated automated system, achieving thoroughness without proportional increases in resource requirements.
4Productivity
If automated processing is implemented, then efficiency and consistency improve, but the system fails to identify areas needing review or flag aberrant data
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously learn from processed data and improve their ability to identify verification areas and flag aberrant information. The automated marking and highlighting functions provide feedback loops that refine the system's detection capabilities, ensuring that efficiency gains do not come at the cost of information loss.
Solution Approach 2:
The system changes parameters by dynamically adjusting detection thresholds and confidence levels based on data characteristics and risk profiles. This allows the automated system to maintain sensitivity to aberrant data while processing efficiently, preventing information loss through intelligent parameter adaptation rather than exhaustive manual review.
Data Source
AI summary
A financial report analysis system having an intelligent automation unit for selecting from stored financial data the financial data to be processed and automatically scheduling for processing the selected financial data, a conversion unit for converting the selected financial data stored in a storage unit into a selected processing format to form converted financial data, a financial data processing unit for automatically processing the converted financial data to form processed financial data and for generating marked financial data from the processed financial data that includes one or more markings, and a verification unit for verifying an accuracy of the processed financial data by comparing the processed financial data to other financial data, and then highlighting or marking portions of the processed data if the data is inaccurate.