Statistical Sampling for Test of Controls, Compliance & Fraud | |||
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Did you know that in many instances, audit samples are LARGER than the statistical sample they could have taken! If, with no extra effort, you can mathematically estimate the characteristics of the whole population, shouldn't you do this? Would it not be great if you could control the risk of reaching incorrect conclusions? Last but not least, enjoy the use of software especially developed by the trainer to make statistical sampling for Auditors child's play! Challenges we will discuss:
Most surveys confirm Auditors desire to use math in their work, but that lack of competences and tools prevent them from making the quality jump. Often auditors look at “symptoms” (e.g., unauthorized payments), missing the opportunity to identify the root cause and suggest long term solutions. Obtain clear results from your tests and focus on root causes to add value to your organization preventing future recurrences of errors and events. In this class you will learn to apply error projection and error isolation. This course will provide several takeaways, including a decision tree to help select the best sampling method, a clear understanding of Alpha and Beta risk and multiple approaches to sample selection. An initial and a final test will allow participants to gain confidence in the acquired competences. With the right teaching approach, even complex matters become simple to understand. Through a practical, pragmatic learning path you will be able to master all you need to proficiently apply a statistical tool especially developed for Internal Audit Teams. There are no prerequisites for attendance however delegates will need to bring their laptops in order to fully benefit from the practical parts of the course |
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Location Milan, Italy | |||
Additional Information about this event: |
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From Wednesday 28 November 2018 To Thursday 29 November 2018 |
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Official Event Website: more info | |||
Number of expected attendees: Not defined | |||
This event has an exhibit: Not defined | |||
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