Audit Interface for Time-Series Outlier Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audit methods lack user-friendly and customizable interfaces for organizing and representing records, leading to inefficiencies in obtaining accurate and reliable audit evidence.
Innovation Solution
An interactive user interface for time series planning, evaluation, and prediction systems that allows users to input audit parameters and receive customizable time series charts with upper and lower acceptance bounds, identifying key items outside these bounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing audit methods are used to organize and represent records, then the audit process can be performed, but the representation lacks user-friendliness and customization options
Solution Approach 1:
The system dynamically adapts the user interface based on user preferences and audit requirements. The interface allows real-time customization of chart types, data groupings, and presentation formats, enabling the same underlying data to be displayed in multiple ways without requiring separate static interfaces for each view.
Solution Approach 2:
The system enables users to modify multiple parameters including time groupings (daily, weekly, monthly), chart types (bar charts, line charts, pie charts), and data aggregation levels. These parameter changes are applied dynamically to transform the representation of audit records while maintaining the core data integrity and analytical capabilities.
2Measurement precision
If detailed analysis of audit records is performed, then accuracy of audit evidence is improved, but time required for analysis increases
Solution Approach 1:
The system automatically segments audit records into meaningful groups based on time periods, account types, or custom criteria. This segmentation allows auditors to focus on specific segments that require detailed analysis while getting automated summaries for other segments, reducing overall analysis time while maintaining accuracy where needed.
Solution Approach 2:
The system applies automated analytical methods to the entire dataset to identify potential issues, then focuses detailed manual analysis only on flagged areas requiring closer inspection. This partial automation approach maintains high accuracy for critical findings while significantly reducing the time required for reviewing the complete dataset.
3Loss of information
If multiple representations of audit records are generated, then comprehensiveness of analysis is improved, but complexity of the system increases
Solution Approach 1:
The system employs a universal data model and processing engine that can generate multiple representations (charts, tables, summaries) from the same underlying data structure. This multi-functional approach allows comprehensive analysis through various view types without requiring separate complex systems for each representation type, reducing overall system complexity while maintaining comprehensiveness.
Data Source
AI summary
There is provided an interactive user interface (1100) for time series planning, evaluation and prediction system to be employed for obtaining audit evidence, wherein audit evidence is to be obtained based upon input data pertaining to given use case, input data being in time structured form, interactive user interface comprising: (i) input interface element that enables user to input plurality of audit parameters; and (ii) output interface element that enables time series planning, evaluation and prediction system to present to user: (a) time series chart representing variation in response variable with respect to time, wherein time series chart indicates upper acceptance bound and lower acceptance bound and is generated for plurality of samples associated based upon input data, plurality of audit parameters and at least one explanatory variable; and (b) key items, wherein key items are identified as samples that fall outside upper acceptance bound and lower acceptance bound in time series chart.