Course Title:Audit Analytics - Rutgers University



Course Title:Audit Analytics Instructor’s Name:Qi LiuDepartment of Accounting & Information SystemsRutgers Business Schoolliuqi67@pegasus.rutgers.eduCourse Number:22:010:688 Term:Fall 2013IntroductionRutgers Business School is introducing a certificate in “analytic auditing” in conjunction with its Master of Accountancy in Financial Accounting (MAccy) Program. 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. BackgroundFor 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: Gain a managerial overview analytical techniquesUnderstand ways in which information systems are used in organizations and industries.Gain understanding of the evolving scenario of big data analytics auditingPerceive the progressive convergence of analytics methods, information processing, and telecommunication technologies.Link audit analytics to corporate continuous monitoring and business process supportThe 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 (). 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 participation30%Assignments 20%Course Project25%Final exam 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: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 e-college. 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 OutlineDue to the state-of-the-art nature of the course versions of the materials and slides will be updated during the course.LectureOutlineMaterialAuthors109/09-09/15IntroductionCompeting on analytics Big data Data Analytics in auditing & Continuous auditing (application areas, evolving approaches, and benefits)Super Crunchers – Ian AiresCompeting on Analytics: The New Science of Winning- Thomas H. Davenport and Jeanne G. HarrisMiklos VasarhelyiQi Liu209/16-09/22Audit Analytics related software & toolsAudit software (ACL/IDEA)Statistical packages (R, WEKA…)Qi Liu,309/23-09/29Audit Analytics in preliminary analytical procedures (I)Descriptive statistics (demonstration using R)Sample dataQi Liu409/30-10/06Audit Analytics in preliminary analytical procedures (II)Data Visualization (demonstration using R)Assignment 1Qi Liu510/07-10/13Audit Analytics in preliminary analytical procedures (III)Basic data analysis (demonstration using ACL)Stratify & ClassifySummarize & Age analysisExam sequence & Look for gapSample dataQi Liu610/14-10/20Audit Analytics in risk assessment (I)Benford analysis (demonstration using ACL)Duplicate analysisField matching (demonstration using ACL)Fuzzy logicSample dataQi Liu, Hussein Issa710/21-10/27Audit Audit Analytics in risk assessment (II)Ratio analysisAssignment 2Assignment 1 dueHelen Brown,Qi Liu810/28-11/03Audit analytics in substantive test Sampling (demonstration using IDEA)Probabilistic sampling Monetary unit samplingVariables samplingCourse project topic dueQi Liu911/04-11/10Predictive audit (I)Regression (demonstration using R)Introduction (concepts and different regression models that can be used in auditing)Selection of regression modelsExample expansionTrevor StewartSiripan1011/11-11/17Predictive audit (II)Expert SystemIntroductionHow to use expert systems to audit and monitor transactionExample expansionAssignment 3Assignment 2 dueMiklos Vasarhelyi,Danielle Lombardi1111/18-11/24Advanced Audit Analytics Techniques (I)ClusteringIntroduction (concepts and, how to use in audit)Different clustering techniques (partitional, hierarchical)Example Qi Liu,1211/25-12/01Advanced Audit Analytics Techniques (II)Text miningText evidences in auditingConcepts of text mining Using text mining to predict audit riskDemonstration of SPLICEKevin Moffitt, Khrystyna Bochkay1312/02-12/08Supporting Technologies/Tools for Audit AnalyticsXBRLData ExtractionCaseware Electronic Working Final Exam Content ReviewAssignment 3 Due Eric CohenQi Liu1412/09-12/15Project Presentation & Final Exam ................
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