1. Chapter 6 Data-Driven Fraud Detection

2. Sampling ISA 240 emphasizes that Fraud is more difficult to detect than unintentional errors Errors- sampling is typically effective Fraud - questionable

3. ISA: Responsibility ISA 240: An auditor conducting an audit in accordance with ISAs is responsible for obtaining reasonable assurance that the financial statements are free from misstatement, whether caused by fraud or error.

4. Scope Paragraph - Standard Report We conducted our audit in accordance with GAAS. Those standards require that we plan and perform the audit to obtain reasonable assurance about whether the financial statements are free of material misstatement.

5. 2 nd and 3 rd standards of field work The auditor must obtain a sufficient understanding of the entity and its environment, including its internal control, to assess the risk of material misstatement of the financial statements whether due to error or fraud, to design the nature, timing and extent of further audit procedures. The auditor must obtain sufficient appropriate audit evidence by performing audit procedures to afford a reasonable basis for an opinion regarding the financial statements under audit.

6. ISA 240: Objectives To identify and assess the risks of material misstatement of financial statements due to fraud; To obtain sufficient appropriate audit evidence regarding the assessed risks of material misstatements due to fraud through designing and implementing appropriate responses; and To respond appropriately to fraud or suspected fraud identified during the audit.

7. Chapter 6: the data analysis process Understand the business Identify possible frauds that could occur Catalog possible fraud symptoms Use technology to gather data Analyze results Investigate symptoms

8. the data analysis process

9. Understand the business – Tour the business – Become familiar with competitors – Interview key personnel – Analyze financial statements Analyze other accounting information – Review documentation regarding processes Accounting Information – Observe employees performing their duties Walkthrough

10. the data analysis process Catalog possible fraud symptoms ----- How will you recognize it if you see it – Accounting anomalies – Internal control weaknesses – Analytical anomalies – Extravagant lifestyles – Unusual behavior – Tips and complaints

11. the data analysis process Identify possible frauds that could occur AU 314.40 “Identify types of potential misstatements”

12. the data analysis process Use technology to gather dataIDEA or ACL Analyze results Investigate symptoms

13. KickbacksRedflags page 171 Figure 6.2 Increasing prices Larger order quantities Increasing purchases from favored vendor Decreasing quality Unusual Behavior - we won’t likely see as external auditors Extravagant Lifestyle - we won’t likely see as external auditors All transactions from one buyer Use of unapproved vendors Anonymous complaints about buyer Complaints from unsuccessful vendors Quality complaints from customers

14. IDEA section 3 -- kickbacks Payments to unauthorized suppliers --- matching payments to list of authorized suppliers extract payments to payees not on list of authorized suppliers Payments to individuals ---- searching for payees without Inc., Co., or Ltd. in their name Test for duplicate invoices duplicate amount and supplier code Duplicate amount and Purchase order number Identify payments made on Sunday Payments just below amount requiring approval (it seems to $80,000) Split invoices Vendors for whom payments have increased more than 25% Match supplier names with employee names

15. IDEA section3 - tomorrow 3.8Stratify payments a lot $75,000 - $80,000 3.9Extract all payments between 75 -80,000 Extract all payments over $100,000

16. IDEA section 3 - tomorrow 3.10Extract payments round numbers $xx,000 Extract all payments authorized by HMV lots of $75-$80 payments were HMV’s Extract all Sunday payments Extract all payments with “CASH” in name Could search for other common names, addresses, PO boxes, phone nos Benford’s Law

17. IDEA section 3 - tomorrow 3.12Duplicate Payments (Duplicate Key) same Amount and Supplier number extract all Supplier Numbers with > one Payee 3.13Search for unrecorded checks (GAP Detection) 3.15Extract suppliers paid in less than 25 days - policy to pay between 25-35 days

18. IDEA section 3 - tomorrow 3.16Join Accounts Payable and Authorized Supplier Two Payees with the same Supplier Number Duplicate Key Extract payments to vendors not on Authorized Supplier 3.17Summarize and Extract vendors for which payments increased more than 25%

19. IDEA section 3 - tomorrow

20. Analysis Bedford’s Law Normal Distribution ± 1.00.6828± 1.28.80 ± 2.00.9544± 1.645.90 ± 3.00.9973± 1.96.95 ± 2.57.99

21. Trend Analysis Over time you see quantity purchased from vendor increasing price per unit from vendor increasing

22. Match or Fuzzy Match Employee names -- vendor names Customer names – vendor names Addresses, PO Box, phone numbers

23. Why might you see One vendor is always the last to submit a bid The procurement specifications include a large number of specifications that do not seem necessary

24. Analyze Financial Statements “change statements” Change current year from prior year Ratios Vertical analysis % of Sales on Inc State----% of Total Assets on BS Horizontal analysis % change from prior years Statement of Cash Flows

25. Liquiditycurrent ratio Quick ratio Acid Test ratio PerformanceAccounts Receivable Turnover / Days in Receivabl Inventory Turnover / Days in Inventory SolvencyDebt to Equity Debt Percentage Times Interest Earned ProfitabilityProfit Margin Return on Equity / Return on Assets / Return on ? Earnings per Share