Data Ingress Tool for Financial Audit Format Conversion
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Solution Overview
Problem
Traditional audits of financial data are labor-intensive and costly, requiring significant manual effort to format data for analysis tools, which often fail to process data correctly due to inconsistencies and varying accounting systems, limiting the ability to identify high-risk transactions effectively.
Innovation Solution
A method and apparatus for semantic processing of data files using a data ingress tool that detects and converts data formats to be compatible with analysis tools by semantically analyzing criteria and performing necessary data manipulation operations, such as grouping entries and generating new transaction IDs, to ensure data is in the correct format for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data formatting is performed by database professionals to enable audit assistance software to process financial data, then the software system can understand transactions and interactions, but the process becomes significantly time-consuming and cost-prohibitive
Solution Approach 1:
The audit assistance system automatically detects data formats, performs semantic analysis, and executes data manipulation operations without requiring manual intervention by database professionals. The system self-services by identifying the source data format through signature recognition, determining appropriate transformations, and converting the data to the desired format autonomously.
Solution Approach 2:
The system performs preliminary data format detection and semantic analysis before the actual data processing begins. By pre-identifying the source format and determining necessary manipulations in advance, the system prepares the data transformation plan upfront, eliminating the need for time-consuming manual formatting during the audit process.
2Adaptability or versatility
If diverse accounting systems with proprietary data formatting are used by businesses, then flexibility in data entry is maintained, but the audit assistance software system cannot properly understand the transactions
Solution Approach 1:
The audit assistance system incorporates a universal data format detection mechanism that can identify and process multiple source data formats from different accounting systems. The signature-based detection approach allows the system to universally recognize various proprietary formats and convert them to a standardized desired format, enabling compatibility across diverse accounting platforms.
Solution Approach 2:
The system introduces an intermediary data format conversion layer between the diverse source accounting systems and the audit analysis engine. This intermediary component performs semantic analysis and format transformation, acting as a mediator that translates various proprietary formats into a universal desired format that the analysis tool can reliably process.
3Ease of operation
If human entry of financial data is used, then data input flexibility is maintained, but the entry consistency over time and across users deteriorates
Solution Approach 1:
The system replaces the mechanical process of manual human data entry with an automated computer-based data extraction and transformation process. By using software to automatically extract data from source systems and apply consistent format manipulations, the system eliminates the variability and inconsistency inherent in human entry while maintaining ease of operation through automated processes.
4Productivity
If sampling of financial data is used in traditional audits, then the review process remains manageable, but the potential to identify transactions with highest risk of improper activities is reduced
Solution Approach 1:
The system performs complete data format conversion and preparation for the entire dataset rather than sampling, enabling comprehensive analysis. By automating the data preparation process for all transactions rather than relying on manual sampling, the system can process and analyze the full population of financial data, thereby identifying all high-risk transactions without being constrained by manual review capacity.
Data Source
AI summary
Methods are provided for semantic processing of data files including detecting formats of data embedded in the data files and converting the data to formats compatible with a data analysis tool. The method may comprise determining if the data file comprises signature characteristics associated with a known data format and, if so, determining a set of data manipulation operations associated with the known data format to convert the data file to a compatible format for the data analysis tool. The method may further comprise semantically analyzing components of the data files to assess formatting across a required set of criterions needed by the data analysis tool and determining sets of data manipulation operations to perform to convert the data file to a compatible format.