AuditNet® launched a new survey on Keyword Analytics in an effort to collect keywords from all audit, fraud, and finance professionals. We invite you to complete this survey, which will give you access to the entire list (already in the thousands of words!). Further, the results of the research will be provided as part of a free CPE webinar for survey participants where each attendee will also receive sample ACL code to assist their key words analytic efforts. This webinar will take place early December and all survey respondents will receive an invitation.
Origin of the Survey
Key Word Analytics recently gained popularity in approach due to searches completed for Foreign Corrupt Practices Act (“FCPA”) and even more recently for financial statement fraud. Traditional approaches to assessing compliance with FCPA and anti-corruption typically involved reviewing policies and procedures against best practice, designating various controls as anti-fraud controls and reviewing controls and documenting sample transactions.
Need for a Worldwide List of Words and an Approach
The new survey serves a need whereby most companies are searching with a thin list of words, mostly self-developed, and mainly researched using Web searches. The word tabulation portion of the survey will collect words (already in the thousands) from all and provide a combined list, summarized into Excel and text formats. There are also plans in the next few months to translate the survey results into a variety of languages while understanding which words are being used the most by organizations. Top words hoped to be identified from an FCPA, financial statement audit, and any other perspective deemed useful (i.e., quality management).
Aside from the tabulation of words, the survey also hopes to better answer the question of……
In any organization, there may be thousands of vendors but it is difficult to determine which vendors are the most ripe for review from an error, fraud, or cost recovery perspective. Usually, these vendors are identified as the business naturally realizes which vendors have the most issues, adjustments, and company time to reconcile their account.
However, waiting for these vendors to show their faces through process errors is a reactive versus proactive approach. In order to identify the vendors that are most problematic now, we can run a variety of reports and string the results together so as to identify those vendors showing or “scoring” the most. In essence, we can mathematically calculate risk for each vendor based on the reports we consider “risky”.
In this post, we will explain using a sample ACL project of four scripts how to calculate a score for a vendor by combining the results for three reports using a key field, the unique vendor number. For more on the article and to download sample ACL data files to run a vendor scoring, click here.
There is nothing more deflating that to identify an audit finding and then to find it was simply a “data mistake”. False positives can stymie any auditors work and confidence in using data analytics. Like any cloud’s silver lining, the false positive provides a bread crumb in the trail towards improved process analytics.
In the duplicate payment area, most-occurring false positives include:
- Remove voided checks / e-payments
- Remove cancelled invoices
- Remove cancelled checks / e-payments
- Remove intercompany account
…and then a more elusive false positive which is the removal of credit value invoices. While the first four false positives could be removed from the initial data extract with a simple EXTRACT statement IF there was a void/cancel/intercompany, the removal of credits to an invoice is based on the pairing of a debit to a credit transaction in the data. This is a little more complicated and will be detailed after explaining the situation. See below where we show two situations where some transactions need to be removed to help the analysis……See more by reading the full article.
In my long travels with ACL software, I am almost embarrassed to admit that one of the largest audit findings made to date was using the ACL Analytics AGE Command and done in about one or two minutes. While much more complex routines and systems have been built in ACL Analytics to detect advanced error/fraud schemes, sometimes the simple approaches work the best. The approach saved tens of millions for the company lucky enough to run its data through ACL Analytics and was identified in the first two weeks of installing the product.
Like most companies starting in analytics, the suggestion was made to first review the accounts payable spend. We therefore worked to extract an accounts payable invoice history file that was provided in my first week on the job. This file listed for each row/record:
- total invoice value
- invoice number
- vendor number
- check number
- invoice date
- entry date and
- check / payment date
With the data in hand, we focused first on data validation which, mind you, is one missing links in audits using analytics. This validation process worked to stratify, age, identify average spend by vendor, and even reconciling some physical invoices to the data. We wanted to make sure that what was on physical invoices was what was in the system and furthermore, that we understood the full population for analysis. Everything checked out from a validation perspective and we concluded that the population was complete for review.
In a stratification we completed using the ACL Analytics STRATIFY command……to read more see the full article.
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