Review of Financial Accounting and Detecting Fraud

Introduction

Fraud, including accounting fraud, accounts for significant activity by the criminal classes and this course sets out to explain what accounting fraud is, how it can be detected, and therefore how it might be prevented. A large emphasis of this course is quantitative since accounting is a quantitative subject and auditors and others need a quantitative perspective on their work. The cases and data coming from the Oil and Gas Industry will be used at relevant places in the course. Delegates are highly encouraged to be ready to discuss their own concerns although it is appreciated that confidentially is a supreme concern in a course such as this.

Objectives

• Discuss the structure and foundations of financial reporting
• Undertake an initial analytical and critical review of financial reports
• Be aware of creative accounting and how it can be determined
• Determine the need for forensic accounting by means of the case study and other analysis
• Appreciate the nature or Benford’s Law and its increasingly important role in the prevention and detection of fraud
• Discuss the role of the auditor in detecting fraud
• Applying predictive modeling to the fraud problem
• Carrying out a variety of statistical tests on financial reports and accounting data 

In summary, this course begins by discussing the source of financial reporting information and how it can be used as the source of information for the detection of fraud. The course then goes on to explore fraud under three major headings:

• Creative accounting
• Forensic accounting
• Detecting fraud

Along the way, the course introduces and develops cutting-edge methods that are used to minimize and eliminate the risks and losses being suffered by modern industry and commerce. 

Training Methodology

This is an interactive course. There will be open question and answer sessions, regular group exercises and activities, videos, case studies, and presentations on best practices. Participants will have the opportunity to share with the facilitator and other participants on what works well and not so well for them, as well as work on issues from their own organizations. The online course is conducted online using MS-Teams/ClickMeeting.

Who Should Attend?

The Review of Financial Accounting & Detecting Fraud training course is aimed at finance and accounting professionals and those for whom the prevention and detection of fraud is a major issue.

Course Outline

Day 1: Financial Accounting Review

• The Background to Financial Reporting
  • Structure of financial reports
  • Regulation of financial reporting
  • Legislation
  • Financial reporting standards
  • Concepts and conventions
  • The framework
• The Analysis of Financial Reports 
  • Ratio analysis 
  • Statistical and graphical analysis of financial reports: how can fixed costs be less than zero?
  •  Detecting bias in accounting numbers
  • Qualitative analysis: what people say and what they might mean
  • Oil and Gas Company Reporting Standards and data will feature significantly in this section

Day 2: Creative Accounting

• What is creative accounting and why is it a bad thing?
  • Earnings management
  • Inappropriate accruals and estimates
  • Excessive provisions
  • Recognizing premature revenue recognition
  • Fictitious revenue
  • Aggressive capitalization
  • Creative income statements: classification and disclosure
  • Problems with cash flow reporting
  • Cases from the energy sector will be used here

Day 3: Forensic Accounting

• The basics of forensic accounting
• The going concern concept in greater detail
• Cases in which forensic accounting was used
• Off-balance sheet finance
• Introduction to Benford’s Law
• Cases from the energy sector will be used here

Day 4: The Role of the Auditor and Detecting Fraud

• The Role of the Auditor in the Prevention and Detection of Fraud
• Deterring Fraud                        
  • The fraud triangle    
• Why?
• Who?
• How?
• The COSO “Internal Control-Integrated Framework,” (COSO Model)
• 20 ways to detect fraud
• Fraud v Erroneous Claims
• Fraud Detection as a Predictive Modeling Problem
• Predicting rare events
  • Predictors
  • Stratified sampling
  •  Stratified random sampling
  • Outliers
• Data mining
• Text mining
• Identifying fraud using time series analysis
• Fraud risk assessments

Day 5: Detecting Fraud

• Further Statistical Analysis of accounting and other data
  • Grouping
  • Ordering  Correlation and regression analysis
  • Hypothesis testing
  • Further application of Benford’s Law
• Chi-Square and Kolmogorov Smirnoff Tests
• Mean Absolute Deviation (MAD) test
• Mantissa Arc Test
• Second-Order and Summation Tests
• Summary case study of the main learning points of the course: delegates will be expected to bring their own experiences and learning highlights to this final discussion.