Chapter11PPT4thedition.pptxInternal Auditing: Assurance & Advisory Services
4th edition
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Data Analytics and Audit Sampling
Chapter 11
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Learning objectives
Understand where best to use audit software to perform audit tasks.
Describe the steps to develop an audit approach for data analysis.
Describe opportunities to expand audit opportunities to be predictive and proactive in internal audit work.
Understand the future direction for use of data analytics in internal audit.
Understand audit sampling and the audit risk concepts associated with sampling.
Know how to apply statistical and nonstatistical audit sampling in tests of controls.
Be aware of alternative statistical sampling approaches used in tests of monetary values.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 1: What Does Data Analytics Mean to Internal Audit?
Chapter 2: The Data Analytics Framework
Chapter 3: Develop a Vision
Chapter 4: Evaluate Current Capabilities
Chapter 5: Enhance People, Process and Technology
Chapter 6: Implement, Monitor, Evolve
Chapter 7: The Future of Data Analytics in Internal Auditing
Chapter 11: Data Analytics and Audit Sampling
Data analytics guidance
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics and risk management
Data analytics can be applied to the internal audit function in several ways:
Historical Perspective – Error detection and quantification
Continuous Review – Continuous monitoring and continuous auditing
Future Perspective – Key Risk Indicators along with predictive and prescriptive analytics
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics framework
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
vision
Implementing data analytics into internal audit is no longer a question of when but how.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics Process
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Use of Data analytics
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Types of Data analytics
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics
maturity model
Strategic evaluation allows for development into the "optimized" maturity level
Assess capabilities in:
People
Process
Technology
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Five phases of data analytics maturity
Ad hoc
Defined
Repeatable
Institutionalized
Optimized
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
People maturity
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Process maturity
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Technology maturity
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics and
data visualization
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Board-directed, data-driven risk decisions
The Perfect Storm
Explosive growth in raw data
Technological advances in data storing and analysis
Looking for data-driven decision making with a board-directed focus on:
Credit risk
Anti-money laundering
High-risk entity analysis
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Key take-aways from the research
Most internal audit functions are in the infancy stage of DA initiatives.
Accessing and understanding data is the first step to a successful DA initiative.
CAEs want visualization and predictive analytic solutions.
Developing in-house staff around DA is a significant challenge.
Momentum around DA is gained through financial results (i.e., how much did this save me?)
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
The future of data analytics
The board looking for data-driven decisions on risk
The C-suite looking for key risk analytics and their relevance to the organization
The ability to “foresee” future risks before manifestation
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
What is sampling
Drawing conclusion about a population based on looking at less than 100% of the items that make up the population.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Auditing sampling
What is the objective of the test?
What is to be sampled?
What are we looking for?
How is the population to be sampled?
How much is to be sampled?
What do the results mean?
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Objectives of tests
Controls
Amounts
Dollars
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
What is to be sampled?
Test of Key Controls
A shipping document exists for each invoice
Accounts receivable subsidiary ledgers reconcile to GL weekly
Each employee and contractor employee has completed required safety training
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
What are we looking for?
Test of Key Control
A shipping document exists for each invoice
Accounts receivable subsidiary ledgers reconcile to GL weekly
Employee/contractor signature or electronic signature for attendance/completion
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
How is the population sampled?
Random (statistical)
Known probability (statistical)
Judgmental (non-statistical)
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Statistical vs nonstatistical sampling
Difference between statistical and non-statistical sampling
Random or known probability of item selection
Random – each item has an equal chance of being selected
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Advantages of statistical sampling
Efficient sample design
Measure sufficiency of evidential matter
Ability to project to population with greater surety based on evaluation of results
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Risks of audit sampling
Sampling Risk
Incorrect conclusion because only looked at part of the population rather than the whole
Non-sampling risk
Incorrect conclusion for other reasons such as human error
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Selecting a sample
Judgmental Sampling (non-statistical)
Intentional bias
Block
Haphazard
Random Sampling (statistical)
Generalized random sampling
Systematic selection
Stratified selection
Dollar unit sampling (PPS)
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Requirements for a
random sample
Population must be defined
Sample unit must be defined
Every possible combination of sampling units must have equal (or known) probability of being selected
Once selected the item must be taken to a conclusion and included in compilation of results
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Types of sampling plans
Attribute
Used to determine the proportion of items in a population that have an attribute of interest
Variable
Variables sampling techniques are used to measure the value of an account balance
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
attribute
Test of controls – Sampling risks
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Attribute (cont’d)
3 factors determine sample size
What is an acceptable risk of over reliance (accepting as OK when it is not)
What is a tolerable error (how much reliance do you need)
Expected population deviation rate
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
examples
Case 1
Population deviation rate is expected to be 2% rather than .5%
Case 2
Risk of over reliance goes from 10% to 5%
Case 3
Tolerable error rate goes from 3% to 10%
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Determining sample size
Expected error rate 0%
Risk of over reliance 5%
Tolerable error rate 4%
Sample size?
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Sample size
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Determining sample size
Expected error rate 1%
Risk of over reliance 5%
Tolerable error rate 4%
Sample size?
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Sample size
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Sampling in small populations
Finite Adjustment Factor
Role
Use when sample is 10% of population
Adjustment
prior example
Expected error rate 0%
Risk of over reliance 5%
Tolerable error rate 4%
n=74
N =200
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Sampling in small populations (cont’d)
Finite Adjustment Factor
Role
Use when sample is 10% of population
Adjustment
prior example
Expected error rate 0%
Risk of over reliance 5%
Tolerable error rate 4%
n=74
N =200
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Evaluating results
Expected error rate 0%
Risk of over reliance 5%
Tolerable error rate 4%
Take a sample of 75
Find 1 error
What do we conclude?
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Evaluating results (cont’d)
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Attribute sampling size tables
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Attribute sampling evaluation tables
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Nonstatistical sample sizes
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Additional
sampling
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Chapter 11: Data Analytics and Audit Sampling
Data analytics
opportunities
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
Internal Auditing: Assurance & Advisory Services, 4th Edition © 2017 by the Internal Audit Foundation.
