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SYSTEMS AND ANALYTICS IN ACCOUNTING

AEM 4225/5225

Fall 2023

Professor: Eric E. Lewis, PhD

Office: Warren Hall 310B     email – [email protected]

Class Meetings:   M/W 8:40, 10:10, and 11:25 Warren Hall B50

Admin Support  Jen Williamsand TBA  – [email protected]

Texts: (materials and software to be provided by instructor)

Prerequisites: Financial Accounting

Credit Hours - 3

COURSE DESCRIPTION

An investigation of the systems and software that capture and store economic information, and of the tools and techniques that support a robust use of that data for the benefit of individual enterprises and greater society.  Topics include “Big Data”, Data Visualization, Optimization Tools, and Support Systems and Databases.  Modules on emergent systems, including Blockchain and AI are also engaged.

OVERVIEW and SPECIFIC LEARNING OBJECTIVES 

Managers are increasingly tasked with sifting through large datasets in search of the evidence that they require to support the decisions that they will make and to chart tactical and strategic maps for their organizations.  Many of the tools and techniques that have been developed or adapted to support this process have great promise for broad application in the business world and beyond.  In this course we will undertake an investigation of many of these tools and seek to discover and demonstrate these extensions of their applicability.   We will also engage these tools in their native functions and gain an understanding of how they are deployed in support of the specific goals of a management team.

In this course students will:

· Gain and demonstrate an understanding of the Systems and Software that support the financial and general informational functions of modern and complex business enterprises.

· Engage these systems in support of the array of decisions that managers and financial support staff disciplines.

· Investigate and develop the analytical tools that support the use of “Big Data” to address questions that reach beyond the boundaries of individual enterprises.

· Further explore the ethical framework within which business professionals and academics employ their skills, and discuss the risks of working with Big Data and the safeguards that are available to address those risks.

· Refine the cooperative work and leadership skills that are critical to success in this field through the completion of analysis and casework in analytical teams.

Johnson Learning Goals

Course Organization

Lectures

Students will be expected to attend the class meetings regularly.  The class discussions are an integral part of the learning experience and are essential for students to attain mastery of the course material. For these reasons attendance will be taken regularly and a record of attendance will be maintained.  If a student knows of an impending absence and wishes to be officially excused from a session, consultation with the instructor prior to the absence is required.  A total of more than 2 unexcused absences during the semester will result in a grade reduction in the overall course grade.

Grading

The following weights will be applied in determining the final grade:

Preliminary Examination 40%

Final Examination 40%

Selected Projects 20%  Due dates and formats to be discussed

Total 100%

The Preliminary Examination will be scheduled during two regular class sessions.  Make-up exams will be given only in rare circumstances and only with prior approval of the instructor.  The Final Examination will be held according to the Dyson School final examination schedule.

Course Outline *

Week  Reading Weekly Case Assignments  

1 Introduction, Environment and overview “3 Shifts..Other Readings” None

2 Big Data – Sources and Scope Tableau Intro Big Deals Case

3-4 Data Visualization with Tableau Tableau Modules 2-5 DuPont Case

5 Advanced Visualization Models Tableau Modules 6-10 Gamify Case

6 Data Driven Decisions and Optimization Alteryx BioPhirma Case

7 Advanced Optimization Excel, Access Demo Case

8 Systems of Capture, Storage, and Organization TBA Case & XBRL

9 Safeguards and Controls including Encryption COSO Reading Case Continued

10 Systems Mapping COSO Swim Lanes

11 Encountering Fraud with Analytics Fraud Audits..ACL Fraud Data

12 Benford Analysis COSO & Posted Benford Case

13 Neural Networks, AI, and “Smart” Systems Readings in IS Guest Speaker

14 RPA, UiPath, Blockchain UI Path UI Practice case

15 Final Tableau Presentations Tableau Case 

Students with Disabilities:

If you have a disability that requires special testing accommodations or other classroom modifications, please notify the administrative assistant by no later than the second week of classes.  We will do our best to accommodate you in any way.  Please do not be shy about coming forward.

Incompletes:

The University policy on incompletes is as follows:  The grade of Incomplete is allowed in the College of Agriculture only when two basic conditions are met: (1) The student has a substantial equity at a passing level in the course with respect to work completed; and (2) the student has been prevented by circumstances beyond the student's control, such as illness or family emergency, from completing all of the course requirements on time.

A grade of Incomplete may not be given merely because a student fails to complete all course requirements on time.  It is not an option that may be elected at the student's own discretion or at the faculty member's discretion.

While it is the student's responsibility to initiate a request for an Incomplete, reasons for requesting an Incomplete must be acceptable to the instructor and meet the above-stated College policy.  The professor establishes specific make-up requirements.  An Incomplete allows a specified amount of time, determined by the student's college of registry, for completing course work.  The instructor has the option of setting a shorter time limit than that allowed by the student's college.  CALS requires that a statement signed by the instructor be on file indicating the reason for the Incomplete and the restriction, if any.

If an Incomplete is not made up as required, a failing grade will be submitted.

Academic Integrity:

Each student in this course is expected to abide by the Cornell University Code of Academic Integrity.  Under the provisions of the Code, anyone who gives or receives unauthorized assistance in the preparation of work at home or during tests in class will be subject to disciplinary action.  Your name on any piece of work is our assurance that you have neither given nor received any unauthorized help in its preparation.  You may assist each other on assignments by answering questions and explaining accounting concepts.  However, you should not allow another student to copy your work directly.  All University policies with respect to cheating will be enforced.  A student who is found to have cheated on an exam will receive an “F” in the course.  

Bibliography

3 Shifts in the Modern Data Environment – Tabluea Whitepaper

Committee of Sponsoring Organizations of the Treadway Commission – Integrated Framework. Foster, P. and Schandl A. – 2019

EYARC Analytical Mindset DuPont Case

EYARC Analytical Mindset Gamify Case

EYARC Analytical Mindset BioPhirma Case

KPMG Swimlanes Case Study

Benford case – Lewis, E. 2021

UI Practic Case – UiPath Learning Team – 2021

EYARC Big Deals case - 2018