Introduction To Audit Data Analytics

Question 1

What is audit data analytics?

Answer 1

Audit data analytics is the science and art
of discovering and analyzing patterns,
identifying anomalies, and extracting other
useful information in data underlying or
related to the subject matter of an audit
through analysis, modeling, and
visualization for the purpose of planning or performing the audit

Question 2

Where can ADA be used during an audit?

Answer 2

At all stages of an audit:
Planning or risk assessment
– Preliminary general ledger account balance analysis
– Nonstatistical trend analysis of sales revenue
– Analysis of customer accounts receivable churn
– Quantity and price analysis of sales revenue
– Process mining from sales order to sales invoice
• Substantive analytical procedures
– Nonstatistical predictive model for rental revenue
– Regression analysis of revenue
• Test of details
– Cash receipts to sales invoice matching procedure
– Three-way match sales invoice, shipping document, and master price list
• Testing the effectiveness of internal controls

Question 3

What are the 2 types of data analysis?

Answer 3

• Quantitative Analysis
– Performed on numerical data
– Accounting data
– Performance data
• Qualitative Analysis
– Performed on unstructured data
– Information from questionnaires
– Customer, employee, and vendor surveys
– Email, text messages, etc.

Question 4

What are the types of data analytics?

Answer 4

• Descriptive Analytics (Past Performance)
• Diagnostic Analytics (Causes)
• Predictive Analytics (Future)
• Prescriptive Analytics (Best Options)

Question 5

What is data mining?

Answer 5

• Data mining uses algorithms to identify data in large data bases. There are many types of algorithms, some of the most common are the following:
– Classification Algorithms
– Regression Algorithms
– Segmentation Algorithms
– Association Algorithms
– Sequence Analysis Algorithms

Question 6

What are some techniques for performing electronic data analysis?

Answer 6

• Retrieve Values
• Filter Data
• Compute Derived Values
• Find Extremes
• Sort Data
• Determine Ranges
• Characterize Ranges
• Find Anomalies and Outliers
• Cluster Data
• Correlate Data
• Contextualization (Relevance to User)
For memory: First retrieve data. The SORT and FILTER it to DETERMINE RANGES and CHARACTERIZE RANGES. Find Extremes and Anomalies. Compute derived values to cluster , correlate and contextualize.

Question 7

What are some data reliability generalizations?

Answer 7

• Reliability is increased when evidence is obtained from independent sources outside the entity
• Reliability generated internally is increased when the related controls, including those over its preparation and maintenance, imposed by the
entity are effective
• Audit evidence obtained directly is more reliable than audit evidence
obtained indirectly or by inference
• Audit evidence in documentary form is more reliable than evidence
obtained orally
• Audit evidence provided by original documents is more reliable than
audit evidence provided by photocopies, facsimiles, or documents that
have been filmed, digitized, or otherwise transformed into

Question 8

How does the auditor respond when faced with the task of evaluating a large number of items in the population?

Answer 8

• The auditor’s response may include one or more of the
following:
– More clearly defining the characteristics of the data that are likely
to be indicative of matters that require an audit response, and then
re-applying the ADA using these more clearly defined
characteristics
– Identifying subgroups within the population of items that initially
appear to warrant further attention, and designing and performing
additional procedures that may effectively and efficiently be
applied to each subgroup
– Applying a different ADA, or another procedure, that might more
clearly identify those items that represent a risk of material
misstatement, control deficiencies, or misstatements

Question 9

How to document audit data analytics?

Answer 9

Guide to Audit Data Analytics
– Objectives of the procedure
– Risks of material misstatement that the procedure intended to address at the financial statement level or at the assertion level
– Sources of underlying data and how it was
determined to be sufficient and appropriate (as
necessary in the context of the nature and objectives of the ADA being performed)
– ADA and related tools and techniques
– Tables or graphics used, including how they were
generated
– Steps taken to access data, including system
accessed and, when applicable, how data was
extracted and transformed for audit use
– Evaluation of matters identified as a result of
applying ADA and actions taken regarding those
matters
– Identifying characteristics of specific items or
matters tested
– Individual who performed the audit work and date
such work was completed
– Individual who reviewed the audit work performed
and date and extent of such review