While giving a presentation on Analytics during a recent event, one of the meeting participants asked how the Audit industry felt about data products created using Analytic processes. On first thought, I consider Analytics to be a form of “analytical procedures”. This was my response but I had to qualify it by acknowledging that I wasn’t sure how different auditing standards addressed the topic. Over the last few days I’ve been able to do some research and pull together a quick synopsis of how the most commonly used Audit standards define the work behind Analytics. In summary my initial impression was pretty close… several of major Audit standards define this type of work and emphasize the reliability of data that underpin Analytic data products.
After 100+ years of being silent on the inadequacies of the statistic behind many “statistically significant” conclusions, the ASA published a new statement harshly criticizing p-values online last week. Here’s a link for those who are interested, but a short synopsis follows: http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1154108
The ASA’s actual statement starts on page 8 and includes the following statements:
“Researchers often wish to turn a p-value into a statement about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither.” Ouch. Continue reading “Statistical Version of 100 Year War”
This post may disturb some “old school” auditors. In fact, used to be a self described old school auditor. If I couldn’t find a work paper in the permanent file, someone was going to get an earful about their ability to keep reliable documentation. But the business sector has evolved at a frightening rate since those days. Surprisingly, many audit professionals still consider sampling and testing to be their go-to procedure.
I’m not knocking the old student’s t-test. It’s still the right tool for some situations. But it’s been harder and harder for me to find those situations in recent years. Most of the subjects I audit now can easily be scrutinized using data mining, or even scripted into an automated monitoring report. Continue reading “Quit Sampling!”
While trying to get this blog to appear higher in internet searches, I ran into an interesting article on the Journal of Accountancy discussing Analytic Auditing. The article is written from an external audit perspective and focuses on two benefits. First analytics provides auditors with greater insights into their clients’ business that help to quickly get up to speed on the external audit customer’s business model. Also the article mentions how analytics provide better service to clients.
The article also states a position that I’ve had since learning analytics as an external auditor… that external auditors’ use of analytics lags far behind that of internal auditors. I think a key reason for this is access to and familiarity with data. As an external auditor it took several weeks for me to gain access to a new client system. Once the client granted my access, I didn’t have much time to pull something useful together. Rarely did my projects have more than one or two models. As an internal auditor, I’ve had the same difficulties getting initial access to the system. But once granted access I can continue to develop models as long as my results are useful to the organization. On certain projects/systems this period lasted for several years and allowed for deep exploration and understanding.
To me one of the most impactful sentences from this article is “The profession [external auditing] needs to achieve a “quantum leap” to redesign audit processes using today’s technology, rather than using information technology to computerize legacy audit plans and procedures. ”