Python in Audit - Revenue Forecasting Using Monte Carlo Simulation
Originally published: 08/12/2020 10:57
Publication number: ELQ-79848-1
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Python in Audit - Revenue Forecasting Using Monte Carlo Simulation

A working paper to develop revenue expectation using Monte Carlo

The template work paper aims to help auditors to develop revenue expectation using Monte Carlo Simulation. Auditors generally use excel for documenting numerical work papers, so creating Monte Carlo simulation for a large number of simulations (say 1 million) is little cumbersome if done in excel. This is where the power of Python comes in handy. Once code is developed it is just a matter of a click to develop the most complicated method in finance.

Attached is the code in IPython format which is a jupyter notebook.

Read your file and provide your desired revenue column in the code and click Run All Cells

You will be provided with visualisation in the form of graph and expected numbers of the likelihood of revenue.

If you don't know how and where to run jupyter notebook file use any cloud-based jupyter notes such as google Colab to run the file.

All required libraries are imported however is the libraries are not installed please use pip install to install them first before importing them.

The detail explanation of the code and how to run the file is provided in the youtube video so please refer for details.

Disclaimer :
The code is provided for the educational purpose the author will not accept any liabilities express or implied for any adverse consequences financial or non-financial.

This Best Practice includes
1 Python code file in jupyter note book

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Further information

To improve the audit quality

If python libraries are working as expected.


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  • Hsuan Ying Wang(last updated: 25/11/2021 08:30)
  • Robert T Dang(last updated: 29/10/2021 10:54)
  • Olivier Hostettler(last updated: 25/08/2021 14:17)
    Very clear information. Thank you , I learn very fast with this series of courses on Python.
  • CK K(last updated: 02/07/2021 15:52)
  • Marcelo Zandomênico(last updated: 08/06/2021 22:32)

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