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How long is your attention span?

January 21, 2013 Leave a comment

Human brain has an interesting tendency, it strives to create explanations (or better word would be models) for the environment it observes. Evolutionary speaking, this trait is highly advantageous, it allows humans to adapt and predict their environment, thus increasing chances of survival. But it also creates false positives, builds models and finds order where none exist, as for example in creating constellations out of a random spray of stars in the night sky. While the night sky is an example of how humans find order in dimensional world, we should also note that false positives may exist in the temporal continuum.

Consider, for example, a recent graduate, who becomes a young aspiring commodities trader circa 2000. It would not take long for her to learn that her peers who advise their clients to buy gold are getting substantially bigger paychecks than she does. So she asks her coworkers for their strategies, their models and explanations for how gold behaves. Wether or not those explanations are correct does not actually matter, as long as they predict higher gold price and do not substantially deviate from industry thinking, none of them can be proven wrong. Fast forward to 2012, our graduate is now a 32-year-old successful senior manager, who built her career in gold trading, and received hefty bonuses for the past 10 years. The reason for her success is her unyielding faith in the value of the yellow metal, her peers who doubted that the gold rise in the past are working for her now, since they were not promoted as quickly.

What should interest us in this discussion is the gold pricing model that this manager developed in her head over the years. It can be as simple as a single sentence, or a complex mathematical model, but the key question we must ask is whether or not it is biased. Is it biased? After all this manager was rewarded for a very particular behaviour for the past 10 years, her peers were punished for predicting lower gold prices, her whole career was based on 10 years of rising gold prices, the echelon of her analysts, senior and junior, have similar behaviour cultivated in them since the first day they join the firm. Are they biased? Would they not try to look optimistically on any kind of evidence presented to them, not try to find an explanation for why the gold should rise again after a dip, just as it did before? Furthermore, this slightly skewed view propagates through the ranks with each one adding an additional twist of optimism until reality becomes grossly distorted. How can a person, in a position of power, be objective when all he/she was presented with during her career is a single side of the coin? Can human attention span be long enough to incorporate macroeconomic forces, which take decades to play out, into mental models that humans so eagerly construct?

For most of us gold trading is of little relevance, but principles at play apply to situations that rest much closer to home. Consider for example a banker, who is measured on his yearly performance, while the loans that he makes are for periods longer than 10 years. Would he care if the loan is defaulted on 7 years from now? Probably not, the loan book could have been sold on or he could have changed jobs several times since then. You can realize how this sort of behaviour could quickly become a reckless one. If banking is not personal enough for you, consider a manager who is responsible for plant maintenance. He is presented with a choice, to save some money now or spend it to upgrade plant’s equipment, if he saves it now then he is rewarded, albeit at the risk that the machine will have a higher chance of a breakdown (but when? 1,2 or maybe 3 years from now?). How long is his attention span?

In conclusion I would like to share several quotes that drive home some of the principles discussed.

Abraham Maslow “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.”

Ralph Linton “The last thing a fish would ever notice would be water.”

Daniel Day-Lewis  “Perhaps I’m particularly serious, because I’m not unaware of the potential absurdity of what I’m doing.”

Several movies: Anatomy of a DisasterHow the banks never lose

And several interesting articles: Morgan Stanley, Warning to Banks

Enjoy!

~Alexey


gold price charts provided by goldprice.org

The Future of Internal Audit – Automated Predictive Controls

November 16, 2012 Leave a comment

The increasing quantity of data produced by today’s businesses is old news (1,2,3), in fact we have written about it previously in our Power of Data post. The resulting shortage of individuals with skills and ingenuity to analyze and interpret all the data generated is also widely discussed (The Rapid Growth of Global Data).

So far the Big Data challenge has been addressed by familiar means. Importance of statistical analysis, open data initiatives, development of robust and unstructured databases, creation of integration methodologies, improvements in well-known statistical software (SAS, SPSS, MatLab) and design of new ones, all these instruments place human intelligence as the key to unlocking value stored in the data.

 But what do humans actually do during data analysis?

In my opinion, what we name as “valuable analysis” is nothing more than a process of building a predictive model. By looking at the data and passing it through statistical tests, we hope to discover relationships among different inputs, to enable us to predict and plan our future actions. For example, we want to know how much of the weekly demand for our product can be attributed to our marketing campaign and how much of it can be attributed to the random lack or factors outside of our control. Alternatively, we might want to know: On what occasions do people use Twitter (or any application X)? What do they talk about? Is the comment positive or negative? How does it affect their driving habits for the day? As you can see, the analysis is a process of collecting data or transposing it into a form usable by statistical software, running tests to find relationships, and creating models that would help us to make decisions. So if our advertisement investment did not produce the results we were hoping for, then we will redirect the investment to alternative projects.

Why do we need human intelligence for data analysis?

The main challenge that computers face in their ability to take on data analysis tasks lies in the fact that a lot of the data is unstructured, comes in a variety of forms, and conceals dynamic relationships. Statisticians, if you will, use their intuition to narrow in on important relationships, able to change the form of data to suit their purposes, and draw on their experience and knowledge, relating one piece of data to another. Computers are not able to do that, they have no purpose when they crunch the data, nor they are able to intuitively know that the amount of hens is directly proportional to the amount of eggs produced.

Will it stay like this?

Technology has been making great strides in enabling machines to decode new forms on information. Online translators are able to convey general meaning of foreign texts with ever-increasing literary prose, computer-enabled telephone support services have become common place (although most of us probably still prefer to shout “customer representative”), text recognition is more or less perfected, picture recognition is the next frontier. Although we will not see drastic changes tomorrow morning, over the span of the next 10-15 years we might expect that some of the data analysis tasks that we associate with human intelligence will migrate into the domain of computer ability.

How does it affect Internal Audit?

In Internal Audit (IA), two main terms are related to the aforementioned developments, they are “technology enabled audits” and “automated controls”.  A snapshot about what automated controls are can be found here (Protiviti- Automated Controls). The majority of automated controls come hand in hand with Enterprise Resource Planning (ERP) packages – expensive, enterprise-wide information systems. Since these ERP systems collect a vast amount of information, internal audit professionals can set acceptable variance limits on a range of input variables. A key point here is that IA-professionals are actually the ones determining what important relationships are, much like in the data analysis process already discussed.

How will it change?

I believe that a key function of internal audit departments in the future will be to maintain predictive automated controls and investigate problems flagged by the system. For example, imagine that we are in a trading business. Each employee, has a keycard to enter the building and a password to log on to the work computer. Now imagine that the computer was logged in, but the keycard was not swiped. Furthermore, a large transaction was placed from the computer. Quite suspicious, isn’t it? On one hand, it is a possible fraud, on the other hand maybe the employee innocuously forgot to swipe his card. The supervisor can be alerted and investigate which one of the two it is. Now, it is essential to understand the distinction between the two ways in which we can set up this kind of automated control. On one hand we can hard code everything, IA staff would essentially create a rule that says “If no card was swiped and the computer logged in, then alert supervisor”. In my opinion, this is a highly inefficient and rigid way of doing it. Alternatively, we can implement a system that monitors streams of data, i.e. card swipes and logins, and allows the system itself to build its own rules for what is “normal”. Under this scenario, the system would see that data stream 1 has an input (card swipe), followed by an input in data stream 2 (computer log in), followed by many inputs of varying degree from data stream 3  (transactions). This pattern repeats day after day, until one day data stream 1 produced no record, data stream 2 still occurred, data stream 3 was abnormal, if several streams produce abnormal results then the system contracts the supervisor for investigation. In this simple example only 3 data steams were used, but we can conceivably add computer ip addresses, employees work phone’s gps locations, etc. With additional streams of collaborative data, the system could be trained to predict potentially hazardous situations more accurately. And, without the need to reconfigure the system to each specific situation, it is possible for IA to delegate the task of building predictive models to computers, while concentrating on the investigation of anomalies.

When will such systems be built? How to build them?

It’s an interesting cross-disciplinary topic, that combines aspects of IT, such as machine learning and data storage, Internal Audit, such as control environment and technology risks, and human psychology.  I would be very interested to know your thoughts and insights!

~Alexey

Accounting is all about numbers, right?

Or alternatively “Accounting is not for me because I don’t like numbers.” These are the most common questions/comments that I hear when people find out that I am doing a Masters degree in Accounting. It is a very common assumption that accountants are the number people, great at math and love structure in their work, wallets and private life, thus accounting is a boring and number-driven profession.

But this is not the case! Well more like its an exaggeration. It is true that accountants use numbers, and it’s true that structure and form both play a major role in this profession, but it is not as mathematical as you would think. Numbers are a tool that accountants use to measure value, and value is a intangible concept, much like “love” or “fun”.

Now comes my favorite example to illustrate my point. Imagine you just bought an apple orchard. It cost you $2,000 but it was your life long dream and you are very content with your purchase. At this point how much is the orchard worth? $2,000 you would probably say. A couple of months are passing by and your orchard starts to bear fruit, how much is the orchard worth now? $2,000? But if you sell the fruits is has you could earn $300, so wouldn’t the orchard’s value be $2300? But wait, are you sure you can sell the apples for $300, not more, not less? How sure? Imagine that an orchard next door and similar to yours was just sold for $5,000. Does that mean your orchard is worth $5,000 now? So as the example illustrated the concept of value is not as defined as one might have initially thought.

It is true that numbers play an important role in accounting. It is a tool for an accountant to express their judgment, they must be proficient and accurate in using it, but it is still a tool, not the essence of the profession. I would argue that accounting is overwhelmingly a science about choice. What accountants aim to do is to document how much a particular item is worth, but in doing so they must identify and select appropriate accounting methods. In some cases no choice is given and accounting standards prescribe how to measure value, but in the vast majority of situations it is much more important to know how the accountant makes a choice, rather than how accurately he/she can do arithmetics.

Source: cartoonstock.com

Alex