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New Statistical Audit Manual
Over the past decade many state revenue departments have retired the traditional block sample in conducting an audit in favor of the properly drawn random sample in the context of Computer Assisted Audit Techniques (CAATs). This is part of a trend of using statistics and spread sheets to enhance the effectiveness of all types of audits including forensic auditing. Our free course provides background on this growing movement.
The manual is for accountants to design and implement statistical audit samples. It will outline the process in a step by step fashion. Included will be an online demo with exercises that helps the student to learn statistical auditing and CAATs monitoring by example.
The Ubiquitous Statistical Audit
What do sales and use tax statistical audits have in common with medical insurance claims audits?
Statistically they are identical. The purpose
of both is to review transactions either for
credits and/or dollar amounts in error requiring
insurance company reimbursement for
overpayments or additional sales and use taxes owed by
the taxpayer. You can save a Demo to your hard
drive to explore the dynamics of proper
statistical documentation.
Free Demo Spread Sheet
Statistical Audit Course
The Demo is an example of variable sampling which, with our course, is an excellent gateway to using modern statistical tools that can unlock a wide range of other statistical services including:
data mining, internal audits; income estimation using IRS specifications; fraud detection; sampling for valuation; market analysis; statistical evidence in litigation; sampling for Medicare and other claims; meta files etc.
It is best to first get experience and a comfort level in working with your clients using the variable sampling statistical audit. Use it as a building block to the more advanced topics listed above.
PowerPoint Statistical Audit CourseCurrent Lesson Topic
Detecting Fraud
BENFORD'S LAW PROVIDES A DATA analysis method that can help alert CPAs to
possible errors, potential fraud, manipulative biases,
costly processing inefficiencies or other irregularities. It is
a relatively simple formula:
p(d) = Log10(1/(1+d))
Benford's Law gives the
expected patterns of the digits in the numbers in tabulated data
such as town and city populations or Madoff's fictitious portfolio
returns. Those digits, in unaltered data, will not occur in equal
proportions; there is a large bias towards the lower digits, so much
so that nearly one-half of all numbers are expected to start with
the digits 1 or 2. The widespread applicability of Benford's Law and
its practical uses is to detect fraud, errors and other anomalies.
There are many examples on the internet of authentic and accurate
data that conforms to Benford’s Law – and the fraudulent and
invented numbers that do not. One famous case of fraud is when the
World Bank, by using Benford's Law, found that a country had
consistently provided false GDP data . That country's dishonesty
only delayed the changes needed to correct years of economic
mismanagement. You could also argue the
World Bank should have also taken action sooner once the fraud was
detected.