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. During the presentation, Andrew will explain what open source software is and why it matters; give an overview of the Python and R programming languages; provide an overview of the appealing attributes and downsides of each language; explain why open source languages should be considered instead of traditional CAATs; demonstrate how machine learning can be effectively applied to audit analytics; and most importantly, provide a real world example of how to begin applying these technologies in your organization. By developing a cursory understanding of the vast and exciting landscape of audit data science, your appetite will be whetted to not only find ways to employ these new technologies in your department, but to strive to become a data evangelist for your organization.

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Andrew Clark, IT Auditor / Internal Audit Data Scientist
Astec Industries, Inc., M.S. Data Science Candidate

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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

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Meet Open Source

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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.

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

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

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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.

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

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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

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So what do we do about it?

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Enter Python (and R)

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What is Python?
"About Python." Python.org. Accessed August 14, 2016. https://www.python.org/about/.
• 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)

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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

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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.

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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

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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.

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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.

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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

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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

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Thank you!
• Email: andrewtaylorclark@gmail.com
• GitHub: aclarkData
• Blog: https: aclarkdata.github.io
• LinkedIn: www.linkedin.com/in/andrew-clark-b326b767