Accounting analytics 3 Day Seminar
Accounting analytics
August 2021
Venue and date to be confirmed
Course overview
Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Dr Steven Firer an acclaimed accounting professor, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more. By the end of this course, you’ll understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy. This course has been designed to help you make better business decisions about the emerging roles of accounting analytics, so that you can apply what you’ve learned to make your own business decisions and create strategy using financial data.
Ratios and Forecasting
This section is ratio analysis and forecasting. Since ratio analysis involves financial statement numbers, we will do a ratio analysis of a single company during the module. First, we’ll examine the company\"s strategy and business model, and then we\"ll look at the DuPont analysis. Next, we’ll analyse profitability and turnover ratios followed by an analysis of the liquidity ratios for the company. Once we\"ve put together all the ratios, we can use them to forecast future financial statements. By the end of this section, you’ll be able to do a ratio analysis of a company to identify the sources of its competitive advantage (or red flags of potential trouble), and then use that information to forecast its future financial statements.
Earnings Management
In this section we are going to examine "earnings management", which is the practice of trying to intentionally bias financial statements to look better than they really should look. Beginning with an overview of earnings management, we’ll cover means, motive, and opportunity: how managers actually make their earnings look better, their incentives for manipulating earnings, and how they get away with it. Then, we will investigate red flags for two different forms of revenue manipulation. Manipulating earnings through aggressive revenue recognition practices is the most common reason that companies get in trouble with government regulators for their accounting practices. Next, we will discuss red flags for manipulating earnings through aggressive expense recognition practices, which is the second most common reason that companies get in trouble for their accounting practices. By the end of this module, you’ll know how to spot earnings management and get a more accurate picture of earnings, so that you’ll be able to catch some bad guys in finance reporting!
Prediction Models
In this section, we’ll use big data approaches to try to detect earnings management. Specifically, we\"re going to use prediction models to try to predict how the financial statements would look if there were no manipulation by the manager. First, we’ll look at Discretionary Accruals Models, which try to model the non-cash portion of earnings or "accruals," where managers are making estimates to calculate revenues or expenses. Next, we\"ll talk about Discretionary Expenditure Models, which try to model the cash portion of earnings. Then we\"ll look at Fraud Prediction Models, which try to directly predict what types of companies are likely to commit frauds. Finally, we’ll explore something called Benford\"s Law, which examines the frequency with which certain numbers appear. If certain numbers appear more often than dictated by Benford\"s Law, it\"s an indication that the financial statements were potentially manipulated. These models represent the state of the art right now, and are what academics use to try to detect and predict earnings management. By the end of this module, you\"ll have a very strong tool kit that will help you try to detect financial statements that may have been manipulated by managers.
The three-day course is case study driven and attendees will able to:
Who should attend?
Facilitator profile
Website: https://stevenrfirer.blogspot.com/
Steven has five degrees and qualified as a Chartered Accountant in 1987 after serving 2 years in the South African Air Force. Steven played 7 test matches for the South African indoor soccer team. He obtained his doctorate in business administration from the University of KwaZulu Natal in 2003.Steven is an IFRS, Corporate Governance and Forensic specialist. He has extensive experience in IFRS requirements including the implementation of new IFRS standards, corporate governance law and implementation and is currently practicing as a forensic accountant. Steven was a professor and has lectured for many years at the University of Witwatersrand, Rhodes University, and Monash University in the fields of accounting, auditing, and finance. He has written and co-written over 25 research papers published in various academic and professional journals. Steven has been a presenter on various accounting topics at international conferences and has facilitated many training sessions on accounting and auditing. Steven served as a member of the American Accounting Association’s Auditing Guidance Committee for 4 years.
Detailed course outline
Day 1
Day 2
Expense recognition red flags
Day 3
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