1
Course Title:
Audit Analytics
Instructor’s Name: Qi Liu
Department of Accounting & Information Systems
Rutgers Business School
liuqi67@pegasus.rutgers.edu
Course Number:
22:010:688
Term:
Fall 2013
Introduction
Rutgers Business School is introducing a certificate in “analytic auditing” in conjunction with its
Master of Accountancy in Financial Accounting (MAccy) Program1. This certificate program can
fulfill a dual purpose. MAccy students may specialize in the area taking these courses as electives,
while non-matriculated students may take the four-course certificate independently.
Background
For reasons that are well known, there is a renewed focus on audit quality in the CPA profession.
The PCAOB regulatory regime, the formation of the Center for Audit Quality (CAQ), initiatives at
major firms, and other indicators attest to this. The profession is more focused on more effective
audit methodologies than it has been for decades.
The development of new methodologies needs to be preceded by basic and applied research that
establishes a sound theoretical foundation and demonstrates that they will work. The need for such
research represents an opportunity for universities to work with audit firms, software vendors and
others.
The following are examples, in no particular order, of the types of areas that are likely to prove
fruitful in the field of analytical auditing: Analytical procedures, Other data Analytics, Continuous
Auditing Integration, Audit Risk/Assurance Model, Elicitation, quantification and expression of
professional judgment, Audit optimization, Fraud detection processes, Systems analysis and
internal control evaluation and Smart navigation of GAAP
Course Description and Objectives:
This course is intended to provide you with the basics of the application of analytics in the (internal
and external) audit process in current ubiquitous computer-based information systems and their
application in organizations. Specifically, you will have an opportunity to begin to:
1.
2.
3.
4.
Gain a managerial overview analytical techniques
Understand ways in which information systems are used in organizations and industries.
Gain understanding of the evolving scenario of big data analytics auditing
Perceive the progressive convergence of analytics methods, information processing, and
telecommunication technologies.
5. Link audit analytics to corporate continuous monitoring and business process support
1
http://business.rutgers.edu/finmaccy/
2
The module does not primarily focus on the technical aspects of analytic methods, though these
topics will be discussed largely in the context of case examples: thus, the emphasis is on the usage
of statistics and the interpretation of results rather than the mathematics of specific tools and
techniques.
Course Structure
This course is an online course, so there is not specific class hour for this course. Classes will be
organized by weeks. Course materials as well as discussion topics will be posted online at the
beginning of each week; you can study the course materials and participate in the discussion at any
time during the week. You can access the course materials under your individual student accounts
at Rutgers Online Learning center (http://onlinelearning.rutgers.edu/ecollege). A comprehensive
instruction about how to use the system will be available after logging in.
Background Textbook References:
We don’t assign any specific text book to this course. All the
lectures will have a set of slides associated to them and some
of them have corresponding videos. You will be able to see the
slides and videos gradually at the beginning of each week on
e-college.
Teaching materials will be drawn from many sources including
the Internet, professional articles, academic articles and books.
The WWW is the Universal Library. Part of the learning of this course should be to understand
how to mine this resource and join it to more traditional sources. Make sure that you reference the
materials you draw from the Internet or from other sources.
Grading:
A module evaluation will be performed based on:




Class participation
Assignments
Course Project
Final exam
30%
20%
25%
25%

Class Participation:
Online chat room is the primary way for the students to communicate with instructions and
each other. Class participation will be evaluated according students’ participation in each
week’s discussion. Students can participate in the discussion by answering instructor’s
questions, posting their own questions, and answering the other students’ questions in the
discussion board in e-college. Both the quality and quantity of the questions and answers
will be assessed.

Assignments:
There
will be 3 individual assignments throughout the semester (Please see the distribution dates
and due dates of assignments in course outline). The assignments will require you to do
some analytic tasks using the tools covered in class. All homework assignments must be
prepared using a word processor and uploaded to e-college prior to the deadline.

Course project:
3
The topic of course project can be of your choice but it would be related to the class topics.
Each course project will “lead” / present a course topic through a presentation of maximum
20 minutes. Students can choose to do the course project individually or in groups. I
encourage students to self-organize into groups of up to 3 students to do the course project.
Each group/student will be scheduled a time slot to make their presentation. During the
scheduled time slots student should make their presentations using “Classlive” tool in ecollege. I will evaluate the presentations based on content, organization, originality, and
delivery.

Final exam:
The final exam will be a remote exam and last for three hours: the exam will be sent to
students via email, and students need to send back their exams in three hours. For exams
you will be responsible for the material covered in the lecture slides, projects and class
discussions. Exams will include six essay questions; students need to choose four of them
to answer. All the students are expected to take the final exam at the same time. If a student
has valid excuse which complies with University regulations for missing an examination,
the student must inform me and obtain permission to miss the examination before the
examination. Failure to obtain the necessary permission will result in a zero grade.
Course Outline
Due to the state-of-the-art nature of the course versions of the materials and slides will be updated
during the course.
Lecture
Outline
1
Introduction
09/0909/15
 Competing on analytics
Material
Authors
Super Crunchers – Miklos
Ian Aires
Vasarhelyi
 Big data
Competing
on Qi Liu
 Data Analytics in auditing & Continuous
Analytics:
The
auditing (application areas, evolving
New Science of
approaches, and benefits)
Winning- Thomas
H. Davenport and
Jeanne G. Harris
2
Audit Analytics related software & tools
09/1609/22
 Audit software (ACL/IDEA)
3
Audit Analytics in preliminary analytical Sample data
procedures (I)
09/2309/29
4
09/3010/06
Qi Liu,
 Statistical packages (R, WEKA…)
 Descriptive
using R)
statistics
Qi Liu
(demonstration
Audit Analytics in preliminary analytical
procedures (II)
 Data Visualization (demonstration using
R)
Qi Liu
4
 Assignment 1
5
10/0710/13
Audit Analytics in preliminary analytical Sample data
procedures (III)
Qi Liu
 Basic data analysis (demonstration using
ACL)

Stratify & Classify

Summarize & Age analysis

Exam sequence & Look for gap
6
Audit Analytics in risk assessment (I)
10/1410/20
 Benford analysis (demonstration using
ACL)
Sample data
Qi
Liu,
Hussein Issa
 Duplicate analysis

Field matching
using ACL)

Fuzzy logic
(demonstration
7
Audit Audit Analytics in risk assessment (II)
Helen Brown,
10/2110/27
 Ratio analysis
Qi Liu
 Assignment 2
 Assignment 1 due
8
Audit analytics in substantive test
10/2811/03
 Sampling (demonstration using IDEA)

Probabilistic sampling

Monetary unit sampling

Variables sampling
Qi Liu
 Course project topic due
9
Predictive audit (I)
11/0411/10
 Regression (demonstration using R)
 Introduction (concepts and different
regression models that can be used in
auditing)
Trevor
Stewart
Siripan
 Selection of regression models
 Example expansion
10
Predictive audit (II)
11/1111/17
 Expert System

Introduction
Miklos
Vasarhelyi,
Danielle
Lombardi
5

How to use expert systems to audit
and monitor transaction

Example expansion
 Assignment 3
 Assignment 2 due
11
Advanced Audit Analytics Techniques (I)
11/1811/24
 Clustering

Introduction (concepts and, how to
use in audit)

Different
clustering
techniques
(partitional, hierarchical)

Example
12
Advanced Audit Analytics Techniques (II)
11/2512/01
 Text mining
 Text evidences in auditing
Qi Liu,
Kevin
Moffitt,
Khrystyna
Bochkay
 Concepts of text mining
 Using text mining to predict audit risk
 Demonstration of SPLICE
13
12/0212/08
Supporting Technologies/Tools for Audit
Analytics
 XBRL
 Data Extraction
 Caseware Electronic Working Papers
 Confirmation.com
 Final Exam Content Review
 Assignment 3 Due
14
12/0912/15
Project Presentation & Final Exam
Eric Cohen
Qi Liu