Luhn Algorithm: A Little Known Tool for Auditors

Buying with Credit CardLuhn Algorithm also known as ‘modulus 10’ or ‘mod 10’ algorithm, was created by Hans Peter Luhn in 1954.
It is widely used in Credit/Debit card numbers, IMEI numbers, and Canadian Social Insurance numbers.

What is Luhn Algorithm?

To understand what Luhn algorithm is, we first need to understand what is ‘modulo’. Modulo or Modulus is the remainder after dividing the number with another number. Consider the example 7 divided by 3 has; quotient 2 and remainder 1.
Therefore, modulo 10 equal 0 means after dividing the number with 10, the remainder should be 0. In simple terms the number (dividend) should be a multiple of 10 (divisor).

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Relative Size Factor: Finding Outliers

In my previous article Auditing: Accounts Payable / Vendor Payments I spoke about Relative Size Factor (RSF) and how it can used to identify isolated outliers in vendor invoices. In this article I’ll try to show how RSF can be calculated in Excel.

The RSF test is an important tool for detecting errors. RSF test compares the top two amounts for each subset and calculates the RSF for each. The test identifies subsets where the largest amount is out of line with other amounts for that subset.

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Auditing vs Fraud Examination vs Forensic Accounting

In general terms, fraud is an intentional deception, whether by omission or commission, to realize a gain. Under common law, fraud includes four essential elements:

  • A material false statement
  • Knowledge that the statement was false when it was spoken
  • Reliance on the false statement by the victim
  • Damages resulting from the victim’s reliance on the false statement

In the broadest sense, fraud can encompass any act for gain that uses deception as its principle technique. This deception is implemented through fraud schemes, specific methodologies used to commit and conceal the fraudulent act. The legal definition of fraud is the same, whether the incidence is criminal or civil. The difference is that criminal cases must meet a higher burden of proof.

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Spreadsheets: Auditing & Validating (Part 2)

00_Spreadsheet AuditIn the previous post I mentioned few of the risks associated with spreadsheets. In this post I’ll try to show some excel tools which can help  in detecting errors and frauds in Excel spreadsheets.

In the late 1990’s “Poor control over spreadsheets at Jamaican indigenous banks contributed to management information and external reporting problems (i.e., P&L distortions) that contributed to the banks’ management and external regulators losing sight of the banks’ true positions and exposures. Which led to collapse of entire Jamaican Banking System.

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Beneish M-Score: Identifying Financial Statement Manipulations

Analyzing Financial DataAccording to the ACFE’s “Report to the Nation 2016” financial statement fraud occurred in less than 10% of the cases reported by the respondents. But it caused the highest median loss of $975,000. Asset misappropriation schemes was reported in more than 83% of the cases with median loss of $125,000. And Corruption cases fell in the middle with 35.4% of cases with median loss of $200,000. Several cases included schemes in more than one category.

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Financial Shenanigans: Earnings Manipulation (Part 2)

In my previous post I mentioned the brief summary of accounting shenanigans identified by Howard Schilit in his book Financial Shenanigans used by management to manipulate earnings, cash flow and key metrics.
Below is the summary of various methods management uses to manipulate the earnings through the 7 Earnings manipulation shenanigans.

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