Open source software is taking the computer science community and IT departments by storm. The breadth of options, the timeliness of updates, the price, and the sense of community are all contributing factors to the rise of open source computing. For many years audit analytics has been confined to the Computer Assisted Auditing Techniques, CAAT, software vendors ACL, IDEA and now Arbutus. However, these software programs require extensive training to use effectively, are not very flexible, and in most cases fail to provide the outcome auditors are expecting. Moving to an open source platform based around the python ecosystem allows for true customization of analytics, and provides a common language to interact with your IT department. By using the same set of tools, an auditing department can move from rudimentary AP duplicate tests all the way to advanced classification and clustering machine learning tests. Although the barrier to entry for open source software is higher than for most CAATs, with cross-functional collaboration, a truly customized, sustainable, and highly effective analytics program can be created.

1. December 8, 2016
Andrew Clark, IT Auditor / Internal Audit Data Scientist
Astec Industries, Inc., M.S. Data Science Candidate
Where Open Source Meets Audit Analytics

2. Overview
1. What is open source software?
2. Why is it important?
3. What are the benefits of using open source software for analytics
over CAATs?
4. How do I begin using open source software for analytics?
5. Case study
6. The application of advanced analytic techniques

3. Meet Open Source

4. Open Source Software
“Open source software is software whose source code is available for
modification or enhancement by anyone.”
What Is Open Source?" Opensource.com. Accessed June 12, 2016. https://opensource.com/resources/what-open-source.

5. Open Source examples
1. Linux (mainly)
2. Android (mainly)
3. Firefox
4. R programming language
5. Git
6. Docker

6. Why is it important?
 Vibrant community
 Frequent updates
 Potential for strong security
 Cutting edge technology
 Customizable
 Cost

7. How does Open Source relate to Audit Analytics?
 State of the art technology
 Computer science's best and brightest love to contribute
 Customizable
 Scalability
 Beautiful visualizations
 Analytics and Data Science leaders use almost exclusively open
source frameworks for their analytics, i.e. Google, Facebook, Uber,
Airbnb, etc.

8. "Bubble Charts." Plotly. Accessed August 14, 2016. https://plot.ly/python/bubble-charts/.

9. Benefits over traditional CAATs
 ACL, IDEA, Arbutus, the existing market leaders
 Not very user friendly
 Requires extensive training to use effectively
 Not very flexible
 Does not provide the output auditors are expecting

10. So what do we do about it?

11. Enter Python (and R)

12.  Open source, general purpose programming
language
 High level of support
 Used by some of the best and brightest in
Data Science
 Extensive scientific, mathematic, data
wrangling and visualization libraries
 Most popular first language in computer
science departments across America
(http://tinyurl.com/knw5mdv)
What is Python?
"About Python." Python.org. Accessed August 14, 2016. https://www.python.org/about/.

13. What is R?
 "R is a language and environment for statistical computing and
graphics."- "What Is R?" The R Project for Statistical Computing. Accessed August 14, 2016. https://www.r-
project.org/about.html.
 Used widely by statisticians for statistical analysis
 As a result of its widespread use, thousands of easy to implement
libraries that provide *all* widely used statistical techniques
 Is not a 'real' programming language

14. How would we go about using Python (or R)?
 The hard way: by learning it
 The even harder way: hire an auditor with programming, analytics
and auditing experience
 The *easiest* and most effective way: create a cross functional team
by borrowing a programmer from IT and a business analyst from the
business.

15. Example Python (and R) analytic test
 https://github.com/aclarkData/AuditAnalytics
 999 amount, weekends and keywords journal entry tests
Steps:
 Input libraries
 Import data
 Wrangle as needed
 Export to folder
 Email
 Schedule - Task Scheduler in Windows, Cron, or equivalent in Unix based system, i.e. Mac and Linux

20. Machine Learning
 In essence, a machine understanding patterns in data without having
to be explicitly programmed.
 Very, very powerful technology that is transforming banking, search
engines, advertising, and soon, every industry.
 Examples: Credit card fraud detection, target demographic advertising,
anomalous sensory data, etc.

21. Machine Learning Cont.
 Numerous possibilities for utilizing machine learning and related technology, e.x.
Natural Language Processing, etc., for Financial Auditing
 For example, unsupervised clustering algorithm in use at Astec Industries.
 Latest developments are only available in open source software or expensive
statistical or computational programs such as SAS, which currently runs at a
minimum of $9,200 upfront per single user license plus annual fees - “SAS® Analytics Pro."
SAS®. Accessed August 26, 2016. https://www.sas.com/store/software/analytics-pro/prodPERSANL.html.

22. Possibilities:
 Time Series Machine Learning for predicting account balances
 Natural Language Processing techniques for contract review and
summarization - current bottleneck is (OCR) Optical Character
Recognition technology.
 Sentiment Analysis for Journal Entry and Transaction descriptions.
 Jupyter notebooks for reproducible analytics and audit
documentation

23. https://try.jupyter.org/

24. Conclusion:
 Definition of Open Source Software
 Unlimited possibilities for a customizable analytics experience
 Scalable
 Real world example
 Machine Learning and the future of audit analytics

25. THANK YOU!
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