A challenging problem in analyzing text-based datasets is that a same record may be represented in multiple ways throughout the dataset. Variations can result from inconsistent naming conventions, spelling mistakes, etc. which makes it difficult to compare text data using ‘VLOOKUP’ & ‘MATCH’ functions. To match text datasets we can use something called Fuzzy Logic.
Luhn 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).
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.
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.
Spreadsheets are known to be error prone. As per one study by Raymond Panko, 86% of spreadsheets contains errors. Errors in spreadsheets can’t be eliminated completely, but steps can be taken to reduce them.
In 2003, “a cut-and-paste error in a spreadsheet cost TransAlta, a Canadian power generation company, $24 million in overpayments for hedging contracts.”
In the previous post Spreadsheets: Risk, 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.
Spreadsheets have stood the test of time because they continue to meet the analytical needs of organizations, especially for analyzing and reporting financial results and providing support for decision-making.
“…spreadsheets will always fill the void between what a business needs today and the formal installed systems…” Mel Glass et al