Audit Evidence Time-Series Sampling for Reliable Data Validation
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Solution Overview
Problem
Existing computing systems lack specialized hardware for efficiently aggregating and organizing diverse audit evidence from disparate sources, leading to errors and inaccuracies in audit planning and procedures.
Innovation Solution
A system comprising a server arrangement with modules for data input, validation, interactive user interface, data analysis, and identification, which performs automated or semi-automated time series planning, evaluation, and prediction to obtain audit evidence, reducing stochastic errors and calculation burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computing devices and manual analytical methods are used, then device complexity is reduced, but measurement precision and reliability of audit evidence deteriorate due to errors and inaccuracies
Solution Approach 1:
The system divides the audit evidence collection process into separate functional modules: data input module, validation module, data analysis module, and identification module. Each module handles specific tasks independently, improving reliability through specialized processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The validation module acts as an intermediary between data input and data analysis, checking and verifying data quality before analysis. This intermediate validation step ensures data reliability and accuracy without requiring the entire system to be overly complex, as the validation function is isolated in a dedicated module.
2Productivity
If manual analytical methods are used, then device complexity is reduced, but productivity and time efficiency deteriorate due to cumbersome and calculation intensive processes
Solution Approach 1:
The system performs self-service through automated data collection, validation, and analysis processes. The modules automatically process audit evidence without requiring extensive manual intervention, significantly improving productivity. The automated validation and analysis reduce calculation-intensive manual work while the modular design keeps system complexity manageable.
Solution Approach 2:
The patent replaces manual analytical methods with automated computational processes. The data analysis module uses computer-based algorithms to perform trend analysis, ratio analysis, and reasonableness testing, substituting mechanical manual calculations with electronic processing. This substitution dramatically improves productivity while the modular architecture prevents excessive system complexity.
3Measurement precision
If specialized computing hardware is provided for data aggregation, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The system uses conventional computing devices that can perform multiple functions through software modules rather than specialized hardware. The same computing platform handles data input, validation, analysis, and identification tasks, achieving precise data aggregation through software-based processing while avoiding the complexity and cost of specialized hardware.
Solution Approach 2:
The system changes the approach from hardware specialization to software parameter configuration. Instead of using specialized hardware, the validation module applies predefined statistical rules and the data analysis module applies analytical parameters to conventional data, achieving precise measurement through parameter-based processing rather than hardware specialization.
4Quantity of substance
If comprehensive data aggregation from disparate sources is performed, then quantity and completeness of audit evidence increase, but device complexity and difficulty of data organization increase
Solution Approach 1:
The system segments the data organization function into separate modules: data input module for collecting data from various sources, validation module for checking data quality, and data analysis module for processing. This segmentation allows comprehensive data aggregation from disparate sources while maintaining low complexity through modular organization of data handling tasks.
Solution Approach 2:
The validation module provides feedback on data quality and completeness, identifying issues with the collected data. This feedback mechanism ensures that comprehensive data aggregation maintains high quality standards while the automated feedback loop simplifies data organization complexity by automatically identifying and addressing data issues without requiring complex manual coordination.
Data Source
AI summary
There is provided a system that, when in operation, obtains audit evidence, wherein the system comprises a sewer arrangement (102) that is configured to: (a) obtain input data pertaining to a given use case for which the audit evidence is to be obtained, wherein the input data is in a time structured form; (b) validate the input data; (c) provide user with interactive user interface to enable the user to input plurality of audit parameters, such audit parameters comprising at least one of: assurance level, tolerable error, statistical sampling technique, time period, level of data aggregation; (d) generate time series chart and identify upper acceptance bound and lower acceptance bound of data points in time series chart; (e) identify key items that are required to be tested, the key items being samples that fall outside the upper acceptance bound and the lower acceptance bound in the time series chart.