24 Jul Please read the three articles. After reading, youll have to? (1) summarize and explain the main points of the articles that you choose from the assigned papers
Please read the three articles. After reading, you’ll have to
(1) summarize and explain the main points of the articles that you choose from the assigned papers
(2) conclude with your own opinion about the issue being discussed in the article.
Minimum 200 words, APA format, need to source 3 articles outside of the reading and properly cite sources.
Artificial Intelligence in Auditing
H-1
©McGraw-Hill Education
1
learning objectives
Define machine learning and artificial intelligence
Introduce the common use of artificial intelligence
Illustrate Robotic Process Automation
Demonstrate artificial intelligence in auditing
2
PwC | Statistical Programming I
2
Data analysis cycle
3
Acquire data
Analyze data
Present findings
Ask a question
1
2
Transform data
3
4
5
PwC | Statistical Programming I
3
What is data science?
4
Adapted from http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
Computer Science
Statistics
Domain Knowledge
Data Science
Danger Zone!
Traditional Research
Machine Learning
PwC | Statistical Programming I
4
Discussion
What have you heard about…
Predictive analytics
Machine learning
Artificial intelligence
What are some examples that use these techniques?
Consumer products
Business solutions
5
PwC | Statistical Programming I
5
Common contributions to machine learning:
Updating e-mail spam filter rules.
Telling newsfeeds (e.g. Flipboard) your preferred content.
Using Google maps and Google translate.
& recommendations based on previous picks.
Alexa, Google Home, Bixby = AI assistants
Data and analytics in industry
7
Trade surveillance
Predictive maintenance
Performance management
Marketing
Spam filtering
Sentiment analysis
Document classification
Facial recognition
Education outcomes
Video search
Recommendation engines
Customer retention
Cybersecurity
Text auto-completion
Regulatory compliance
Customer service
Fraud detection
Translation
Self-driving cars
PwC | Statistical Programming I
7
What is predictive analytics?
Predictive analytics is the systematic computational analysis of historical data to predict unknown values or states of the world
A predictive model is a description of the relationship between one or more variables (X) and another variable (y) that enables us to:
Quantify and determine the significance of effects of X on y
Predict new values of y given new values of X
Assess the quality of the model and resulting predictions
Predictive models fall into two general categories:
Regression models predict values
Classification models predict states
8
| y | X |
| Dependent | Independent |
| Predicted | Predictor |
| Response | Control |
| Explained | Explanatory |
PwC | Statistical Programming I
8
What is machine learning?
Machine learning is a field of computer science relating to algorithms that can recognize patterns and make predictions on data
Unsupervised learning
There is no single dependent variable
The algorithm is used to identify patterns and anomalies according to similarities and differences among observations
Supervised learning
supervised learning ≈ predictive analytics
There is a dependent variable and at least one independent variable
The algorithm is trained on historical observations with known values or states and used to make predictions about new observations
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PwC | Statistical Programming I
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Machine learning and AI
In traditional programming, the computer consumes data and generates an output according to a given set of instructions
A machine learning algorithm takes input data and known outputs to learn the program (a statistical model) that should be applied to new inputs
Artificial intelligence (AI) is a field of computer science relating to computer systems that can perform tasks typically requiring human interaction
Machine learning algorithms and very large training data sets often provide the predictive power behind an AI system
10
Traditional Programming:
Machine Learning:
Input + Program Output
Computer
Input + Output Program
Computer
PwC | Statistical Programming I
10
How AI terms are related:
A subset of AI that includes abstruse statistical techniques that enable machines to improve at tasks with experience. The category includes deep learning.
The subset of machine learning composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data.
Any technique that enables computers to mimic human intelligence, using logic, if-then rules, decision trees, and machine learning (including deep learning)
Machine Learning
Artificial Intelligence
Deep Learning
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
Why use AI?
Potential advantages of AI:
Rapid response times
Analysis of large quantities of data
Reduced labor costs
Reserve use of human judgement for hard problems
Lack of bias?
Potential challenges of AI
AI does not have “common sense”
May not respond optimally to new and unusual events
High up front costs
Legal and ethical questions
Inherent bias?
12
PwC | Statistical Programming I
12
Artificial Intelligence is a productivity tool
Accountants have used tools to support their roles for centuries.
Artificial Intelligence = software automation like an Excel macro.
You provide instructions and the software follows through.
Leverage AI as you would a calculator.
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
When will AI affect accountants?
Planned: Bigger, public companies with many transactions have the financial resources and the best case for ROI in regards to automating accounting tasks.
There needs to be good documentation and standardization to base workflows that can translate well to automation.
Unplanned: You may be pushed into using AI in the form of risk management.
Example: AI anti-virus will be used to defend against
AI driven cyber attacks.
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
Introduction to RPA
Robotic Process Automation (RPA): the use of a software robot or “bot” that replicates the actions of a human to execute tasks across multiple computer systems.
A minute of work for a robot is equal to about 15 minutes of work for a human. – (Deloitte)
Robotics is predicted to automate or eliminate up to 40 percent of transactional accounting work by 2020.
– (2015 Accenture report)
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
IMAGE: https://www.alten.com/alten-designs-a-unique-approach-for-effectively-developing-rpa-projects/
15
What can be automated with RPA?
Document Capture Data Entry
3-Way Matching
GL Coding
Vendor Interactions
Payments
Document Review & Approvals
Process Management & Controls
Exception Processing
And more…
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
https://www.processmaker.com/blog/how-do-banks-benefit-from-robotic-process-automation-rpa/
What can Robotic Assistants do?
Control processes
Enforce rules
Automate communications
Provide reminders
Manage resources and escalates
Perform data entry
Collect and present data and documentation
Ask for your expert input for review or approval
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
What can Robotic Assistants do?
Control processes
Enforce rules
Automate communications
Provide reminders
Manage resources and escalates
Perform data entry
Collect and present data and documentation
Ask for your expert input for review or approval
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
18
Automated Data Entry
Invoices
Purchase Requests
Purchase Orders
WEB Docs/E-mail
Eliminate Double Entry
Key-Process Docs
Orders
Special Transactions
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
You can start RPA anywhere…
Purchasing
Accounts Payable
Accounts Receivables
Order Entry
Customer
Service
Operations
Compliance
Human Resources
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
Benefits of Accounting Automation
More Efficient Process Less Labor Fewer Mistakes
Ensure Accountability &
Compliance
Rapid
Implementation
Satisfied Users Do More
With Less
BENEFITS
COST TO PROCESS
$22.75
TIME TO PROCESS
16.3DAYS
EARLY PAYMENT DISCOUNTS
16%
78%
REDUCTION
63%
REDUCTION
298%
INCREASE IN
PAYMENTS
before.
RESULTS
COST TO PROCESS
$5.03
TIME TO PROCESS
6.1DAYS
EARLY PAYMENT DISCOUNTS
47%
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
21
Why prepare for AI?
The benefits of AI are expected to outweigh the cons. AI will continue to grow in nearly all aspects of our lives.
AI is a designed to be a problem solving technology.
The world economy is expected to gain billions in GDP through utilizing AI.
Your career will be impacted by this coming change in AI and other related technologies.
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
How to prepare for AI?
Learn the difference between what AI can do easily and what will be difficult for AI to do.
Develop new skills that will benefit from AI driven data.
Be vigilant to keep your data accurate.
Expect routine, rule-based manual tasks to be the first activities to be automated.
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
Examples of AI related to Accounting & Finance
Risk management
Audits
Compliance and reporting
Forecasting and analytics
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
Example using AI
Today’s Automation with an ERP System
Automation through rules-based work flows by user-defined fields
Automatic sub-ledger reconciliations , like AP Trade
Automatic job-costing
Automatic reporting
FIFO inventory by purchase order for automatic proper COGS posting
Application Programming Interface (API) ready for RPA integration
API ready to connect to other databases to push and pull information
Dave Sackett, Get Ready For Artificial Intelligence, CFO University
https://www.noitechnologies.com/artificial-intelligence-and-erp-a-match-made-with-codes/
25
,
Auditing (with) Artificial Intelligence
17 mei 2018 Van der Valk Hotel Utrecht
Mona de Boer, PwC
Who had a customer service interaction in the past month?
2
Who is 100% sure the customer service interaction was with a human?
3
4
Exponential Growth and Advanced Technologies
5
x
1450 2000 210019601790 2030
Book printing InternetPersonal computerSteam machine Rise of AI No one knows?!
“AI will become more powerful than a human
brain”*
Open source machine learning
Algorithms improve rapidly by collaboration and joint development
Affordable high performance computing High-performance computers today are thousand times more powerful than they were 15 years ago
Big data Since 2006, the worldwide data volume has increased tenfold
*Ray Kurzweil, Google
Robotic Process Automation – The Next Big Thing
6
Extension to classical RPA approach by using Artificial Intelligence (AI)
Classic rule based robots excel at AI excels at
Algorithmic processing
Repeatable tasks
Workflow interaction
Natural Language
Pattern identification
Locating knowledge
Machine learning
Eliminate biases
Business process automation platforms
Robotic Process Automation (RPA)
Natural Language Processing (NLP)
AI / Cognitive computing
Algorithmic business Human work
+
Almost half of all processes can be automated by
Classic RPA…
…but most of these automated processes have to stop at human interaction. They can be
unleashed by using Cognitive Automation
Robotic Process Automation – Developing the Robo Auditor
7
Algorithmic Business
▪ Industrialized use of complex mathematical algorithms to drive improved business decisions or process automation for competitive differentiation
Robotic Process Automation (RPA)
Alias: Robotic Desktop Automation (RDA)
▪ Automating labor-intensive, repetitive activities across multiple systems and interfaces by training and/or programming third-party software to replicate a user’s workflow
▪ Operates at the presentation layer without the need to change existing systems
▪ Users intervene to handle exceptions as they arise
Business Process Automation (BPA)
▪ Reengineering existing business processes by using software, integrating systems, and restructuring labor to optimize workflows and minimize costs
Intelligent Process Automation (IPA)
Aliases: Cognitive Computing, Smart Workflows
▪ Combining RPA with artificial intelligence technologies to identify patterns, learn over time, and optimize workflows
▪ Through “supervised” and “unsupervised” learning, algorithms make predictions and provide insights on recognized patterns
▪ With IPA, robots can replace manual clicks (RPA), interpret text-heavy communications (natural language processing), make rule-based decisions that don’t have to be pre- programmed (machine learning), and offer customers suggestions (cognitive agents)
Today Future
How do RPA and IPA differ?
RPA directly mimics human behavior
IPA learns how to become more efficient
ProgramInput Output
ProgramInput
Learning
Output
improve systems simple manual activities and learn complex activities automate automate
judgement automate
8
Journal entry testing today…
Soon to be…
Identifying risks at a level that humans can’t
9
The power of combining exploratory and confirmative analytics
10
Intelligence
Knowledge
Information
Big Data
Exploratory
Confirmative
11
Finding outliers in A/R listing today…
A/R listing anomaly detection through Machine Learning
12
Reviewing MD&A today…
MD&A review on steroids with Natural Language
Analysis & Classification
Visual/Image Recognition – a different view on stock count?
13
Digital Auditing Assistant: Auditing Support through Voice Recognition
14
Blurred lines: Who ‘owns’ the algorithms?
15
PwC
Who audits the algorithms?
16
• Every company will become an IT / analytics firm
• In the short term we will be utilizing algorithms in auditing
• In the long run we will be auditing algorithms
Opening AI’s black box
17
18
Bye bye black box…
Researchers teach AI to explain itself
19
20
Be open-minded about your future colleagues…
,
1
Master Thesis, 15 credits, for
Master degree of Master of Science in Business Administration:
Auditing and Control
FE900A VT20 Master Thesis in Auditing and Control
Spring 2020
Integration of Artificial Intelligence in Auditing:
The Effect on Auditing Process
Aurthors:
Salim Ghanoum
Folasade Modupe Alaba
Supervisor:
Elin Smith
Co-Examiner:
Timurs Umans
E-mail:
2
Abstract
Business growth comes with complexity in operations, leveraging on the use of technology-
based decision tools are becoming prominent in today's business world. Consequently, the audit
profession is tuning into this change with the integration of artificial intelligence systems to
stay abreast of the transformation.
The study is a qualitative research. It adopted an abductive approach. Data used for the study
was collected through a semi-structured interview conducted with auditors from auditing firms
within Sweden that has adopted the use of AI-based tools in their audit process. As a result of
exponentially increasing data, auditors need to enhance the processing capability while
maintaining the effectiveness and reliability of the audit process. The study strongly agree that
the use of AI systems enhances effectiveness in all stages of audit process as well as increases
professionalism and compliance with standards. The study however favored the use of AI-
enabled auditing systems as opposed to the use of traditional auditing tools.
Acquiring adequate skills in handling the AI tool and sound professional skepticism of
auditors was seen to be an underlying factor that would further boost the interaction between
AI tools and audit process. This prompted the need to modify the initially drawn research model
to include skills in handling IT tools and audit professional competency. This which
substantiated the abductive approach of the study.
Keywords: Artificial Intelligence (AI), Audit Process, AI in Auditing, Audit Effectiveness
3
Acknowledgement
Our profound gratitude goes to God almighty for the grace to focus despite the fear of
uncertainties during this difficult time of Covid-19 pandemic in the world. We immensely
appreciate our master’s thesis supervisor Elin Smith, for her commitment shown through
tireless review of our work and her guide all through the study. Our appreciation also goes to
the auditors that accepted our request, created time for the interviews and contributed by
sharing their opinions and experiences on the phenomenon been studied. We also thank our
fellow students for their constructive criticism of the work. It gives a good insight for improving
the work. Lastly, we appreciate our friends and family for their support always.
As a Swedish Institute scholarship holder, I would like to appreciate and acknowledge
Swedish Institute for the opportunity and support for the master’s programme. My contribution
to the study is part of my research work done during the scholarship period at Kristianstad
University, which is funded by the Swedish Institute.
Folasade Modupe Alaba
______________________________ _______________________________
Salim Ghanoum Folasade Modupe Alaba
Kristianstad, 03-06-2020 Kristianstad, 03-06-2020
4
Table of Content
Abstract …………………………………………………………………………………………………………………….. 2
Acknowledgement ……………………………………………………………………………………………………… 3
CHAPTER 1 ……………………………………………………………………………………………………………… 6
1. INTRODUCTION ……………………………………………………………………………………………. 6
1.2. Problematization……………………………………………………………………………………………. 8
1.3. Purpose of the study …………………………………………………………………………………….. 11
1.4. Research question ………………………………………………………………………………………… 12
CHAPTER 2 ……………………………………………………………………………………………………………. 13
2. Theoretical Framework ……………………………………………………………………………………. 13
2.1. Theoretical Model ……………………………………………………………………………………….. 13
2.1.1. The Agency Theory ………………………………………………………………………………. 13
2.1.2. The stakeholder theory …………………………………………………………………………… 14
2.1.3. The theory of inspired confidence …………………………………………………………… 15
2.1.4. The credibility theory …………………………………………………………………………….. 16
2.2. The process of auditing ………………………………………………………………………………… 16
2.3. Artificial Intelligence …………………………………………………………………………………… 19
2.4. AI in Auditing …………………………………………………………………………………………….. 19
2.5. Audit Effectiveness ……………………………………………………………………………………… 20
2.6. Audit Ethics ………………………………………………………………………………………………… 25
2.7. Professional approach to the Adoption of AI …………………………………………………… 26
2.8. Research Model …………………………………………………………………………………………… 29
CHAPTER 3 ……………………………………………………………………………………………………………. 31
3. Methodology ……………………………………………………………………………………………………… 31
3.1. Epistemology position/ Interpretivism ……………………………………………………………. 31
3.2. Ontology Position/ Constructionism ………………………………………………………………. 32
3.3. Data Collection ……………………………………………………………………………………………. 32
3.4. Sampling Method ………………………………………………………………………………………… 34
3.5. Interview Process ………………………………………………………………………………………… 35
3.6. Interview Guide …………………………………………………………………………………………… 36
3.7. Interpreting the data: Structure used for the analyses ……………………………………….. 37
3.8. Bias in data collection ………………………………………………………………………………….. 37
3.9. Trustworthiness, Credibility and Authenticity of the Study ………………………………. 38
CHAPTER FOUR …………………………………………………………………………………………………….. 39
5
4. EMPIRICS, ANALYSIS AND DISCUSSION……………………………………… 39
4.1. Demographic Information …………………………………………………………………………….. 39
4.2 Competence in the use of IT tools ………………………………………………………………………. 42
4.2. Personal views on the importance of automation of the auditing process for the audit
profession …………………………………………………………………………………………………………….. 43
4.3. Auditing Process …………………………………………………………………………………………. 46
4.4. The role AI plays in the process of auditing ……………………………………………………. 50
4.5. Scale rating …………………………………………………………………………………………………. 51
4.6. Ethical concerns ………………………………………………………………………………………….. 52
4.7. Challenges during the implementation of AI systems ………………………………………. 53
4.8. Compliance to the international auditing standards ………………………………………….. 55
CHAPTER FIVE ……………………………………………………………………………………………………… 58
5. RESULT AND CONCLUSION …………………………………………………………………………… 59
5.1. Theoretical and Practical Contribution …………………………………………………………… 60
5.2. Limitation of the study …………………………………………………………………………………. 60
5.3. Future Research Agenda ………………………………………………………………………………. 61
References…..……………………………………………………………………………….. 62
Appendix 1 …………………………………………………………………………………. 73
Appendix 2………………………………………………………………………………….. 74
6
CHAPTER 1
1. INTRODUCTION
1.1. Background to the Study
Technological advancement is transforming the world at an ever-increasing pace. Business
g
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