Auditing and Data Science
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General presentation listing some of the newest tools for data discovery, anomaly detection, visualization and model building. Traditional audit software is fine for answering questions that you have already formulated but poor for discovery and advanced analytics. The audit community needs to close the gap and adopt more current tools.
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Auditing and Data Science
- 1. Auditing & Data Science Narrowing the Gap
- 2. Traditional tools will continue to evolve 3/16/2020 2 EXCEL, ACL, IDEA, AND OTHERS WILL CONTINUE TO ADD FEATURES THEIR ORIENTATION IS TOWARD “KNOWN UNKNOWNS” RATHER THAN “UNKNOWN UNKNOWNS”
- 3. Data science & Complexity •Why sample? •Are visualizations good enough? •Do you have tools of discovery, such as forecasting, clustering, and anomaly detection? 3/16/2020 3
- 4. Analytics tools advance daily but that’s not the most important thing … • Profound changes in the philosophy of data science • Underlying algorithms are moving from simply following instructions to “thinking” (at least a little). 3/16/2020 4
- 5. This presentation does not include Machine Learning … 3/16/2020 5 Machine learning routines require considerable expertise and smarts It also means the surrender of humans to the machines for a narrow section of thought/prediction Theoretically, from the 1930’s (Alan Turing) to the early 2000’s – you could look at the program code and follow its progression Machine learning is only a WRAPPER of code. Its real behavior comes from millions & billions of data points. Not possible to understand.
- 6. Tools & Algorithms • Clustering – where’s Waldo on steroids • Prediction – yea, this is way more than Excel • Gaps & discontinuities • Credit card fraud • Sentiment analysis: Like my stuff? Think it stinks? • Text analytics: Billy Bob (purchasing) next to Sally Mae (vendor) in 10K emails? • Anomaly detection • R has more than 12K packages, many of them vertical apps 6
- 7. Many R packages are industry specific. Some are even understandable. Python has many as well 3/16/2020 7
- 8. What does Harvey talk about in his emails? 8 What is not said is as important as what is said. For example, you do not see the phrase “Bill, you ignorant pig ….”
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- 10. Let’s run some code 3/16/2020 1 0
- 11. Stuff to think about 3/16/2020 11 ANOMALIES BIAS COMMON SENSE SAMPLING CHERRY PICKING LONG TERM MANAGEMENT CAPITAL HEDGE FUND. DUH! DEAR HERR DOCKTOR PH.DS – PLEASE BE AWARE THAT NOT ALL POPULATIONS ARE “NORMAL.” YOU GOT THE TAILS WRONG AND WE (TAXPAYERS) PAID FOR YOUR IDIOT MISTAKE
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- 14. Kudos to Microsoft/Excel. Enter dates and values. Highlight. Click data, forecast sheet. You can specify the number of periods ahead you want. This is a TIME Series projection (better than moving average). 3/16/2020 14 Data = New Zealand government core expenditures since 1972
- 15. 3/16/2020 15 Data science thinking: How do you look at a large log of, for example, 20,000 time stamps to determine if the log shows someone turned off recording for a few minutes or hours? One approach: create an artificial file (via a program or even Excel) with all dates/times for the period. For example, if the log collects data every 5 minutes, show 1/31/20 8:00am, 1/31/20 8:05am, and so on. Perform an “anti-join” to show records in your artificial file with no match on your log file. This could be done in R with an actual antijoin statement or in Excel using Vlookup. Any other ways?
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- 17. GUIs are great for exploration. Excel Pivot tables, with timeline and slicer are cool 3/16/2020 17
- 18. Data science is as much about graphics as anything else. Complexity not required. 3/16/2020 18
- 20. 3/16/2020 20
- 21. This stuff is fun … call or email me with questions, ideas to solve audit problems or suggestions for research. Thanks for your time today. • Bill Yarberry, CPA • ICCM Consulting • 713.582.6275 • byarberry@iccmconsulting.net 3/16/2020 21
Editor's Notes
- Discuss how audit has fallen behind simply because data science is evolving so quickly
- I’m not suggesting in any way that audit throw away the traditional tools; but they are not oriented to discovery
- I’m not suggesting in any way that audit throw away the traditional tools; but they are not oriented to discovery
- Early days of statistics – get the most information out of the least possible data; now what are the possibilities if I have millions, billions or even trillions of points of data.
- Not enough time but it is an extremely important topic. Robots in factories, identification of rogue behavior, just about any narrow expertise can be gained. I ran a ML routine for determination of breast cell malignancy – 30 characteristics, 98% accuracy.
- How would you do it? One variable distance from another, easy. Millions of trans per second. Is this fraudulent? Age, income, $ history, sex, location, abrupt shift. Sentiment analysis: lexicon of good and bad words (various ones).
- Some tools are really easy to use.
- Diederik Stapel, Dutch psychologist, found to have falsified data in 30 papers. Extreme values more probable than in normal distributions. Russia defaulted. Late 1990s
- M,athematical skepticism. Think rational, dispassionate, not ape-to-ape social dominance. SSC is a legitimate trading algorithm
- Rattle, plot.ly
- Don’t make viewer work too hard.
- What is the sweet spot between too much time devoted to analytics and missing valuable information? What are some ways to improve data awareness & opportunities? Where to get objective advice? Nobody pushes tools they can’t make money on.
- What is the sweet spot between too much time devoted to analytics and missing valuable information? What are some ways to improve data awareness & opportunities? Where to get objective advice? Nobody pushes tools they can’t make money on.