Definition: ADA refers to techniques enabling auditors to analyze and review financial and nonfinancial data to discover patterns, relationships, and anomalies during an audit.
Applicability: ADAs can be used in risk assessment, tests of controls, substantive procedures, and to assist in the overall conclusion of an audit.
Benefits: Enhanced understanding of clients, advanced risk assessment, expanded audit coverage, insights from data evaluation, increased efficiency, improved fraud detection, and better communication through data visualizations.
Steps in Applying ADAs:
Plan the ADA, determining its objective and purpose.
Access and obtain the relevant data.
Review and analyze the relevance and reliability of the data.
Perform the ADA using selected tools and techniques.
Evaluate and address the outcomes to ensure the objective is met and determine if further refinement or procedures are needed.
Tools and Technology: Utilized tools include spreadsheets, data transformation and cleaning software, data analytic software, data visualization software, data mining software, programming software, and robotic process automation (RPA) software.
Techniques:
Descriptive Analytics: Explain what happened with the data using summary statistics, data sorting, aging data, and data reduction.
Diagnostic Analytics: Understand why something happened by uncovering correlations, patterns, and relationships using techniques like clustering and variance analysis.
Predictive Analytics: Make predictions about future events using regression analysis, forecasting, and sentiment analysis.
Prescriptive Analytics: Prescribe actions to optimize decisions using techniques like what-if analysis, decision support, automation, machine learning, and natural language processing.
Risk Assessment: ADAs can identify and assess risks of material misstatements at both the financial statement and assertion levels, as well as assess fraud risk.
Test of Controls: ADAs can evaluate the design and operating effectiveness of internal controls by validating control outcomes, analyzing internal data, identifying anomalies, and aiding in reperformance activities.
Substantive Procedures: ADAs can be applied to tests of details and analytical procedures to detect material misstatements in financial statements.
Concluding the Audit: ADAs can assist in forming an overall conclusion by ensuring no material misstatements went unidentified and by incorporating updated financial figures or disclosures.
Data Sources: Data can be sourced from information systems (AIS, MIS, EIS, DSS, CRMs, SCM, IMS, KMS, ERPs), data storage functions (databases, data lakes, data cubes, data warehouses, data marts), internal and reporting sources (audited financial statements, transaction logs, subledgers, general ledgers, source documents), and external sources (governmental, private, and service organizations).
Data Types: Data can be structured (organized, consistent formats) or unstructured (original, unmodified format). Structured data includes information systems, spreadsheets, databases, while unstructured data includes social media posts, interview transcripts, sensor data, videos, and images.
Attributes to Evaluate: Data may be numeric, text, time, or geographic, depending on the ADA's objective.
Data Sourcing Techniques: Techniques include using built-in reporting, custom queries, data mining, data-pulls, and walk-throughs and interviews with clients.
Reliability Verification: Procedures to verify data reliability include flowcharts, tests of controls, confirmations, recalculations, GITC testing, spreadsheet controls, SOC 1® reports, and sequence tests.
Using Data Visualizations: Effective visualizations transform complex data into easy-to-read formats, aiding in understanding and decision-making. Common types include scatter plots, pie charts, bullet charts, and line charts.
Interpreting Results: Techniques include regression analysis, variance analysis, period-over-period analysis, classification, and trend analysis. Visual aids help identify relationships, trends, and outliers, driving further audit procedures if necessary.
Evaluating and Grouping Potential Misstatements: Potential misstatements identified through ADA are categorized as clearly inconsequential or not clearly inconsequential, leading to further procedures based on their evaluation.
Amber is currently auditing Blissful Springs. Blissfu l Springs is a completely outdoor retail chain and does not typically have many, if any, sales during cold or rainy days. Amber wants to perform an audit data ana lytic that correlates sales with weather data. Which of the following approaches to obtaining the weather data would be the best way to enhance that data's reliabi lity?
Amber should request that Blissful Springs provide a report of daily weather information.
Amber should obtain the weather data internal to Blissful Springs using query language.
Amber should discuss with management weather patterns and impact and document the results.
Amber should utilize an external government website that maintains weather data.
Jackson wants to present the findings of his audit data analytic to his manager. He has decided to run a simple linear regression analysis to determine if utility expenses correspond with daily temperatures. Which visualization technique will aid Jackson in presenting and reviewing this audit data analytic with his manager?