Showing posts with label analytics. Show all posts
Showing posts with label analytics. Show all posts

## Wednesday, March 11, 2020

### Benford's Law: finding fraud and data oddities

What links fraud detection, old-fashioned log tables, and error detection in data feeds? Benford’s Law provides the link and I'll show you what it is and how you might use it.

Imagine I gave you thousands of invoices and asked you to record the first digit of the amount. Out of say, 10,000 invoices, how many would you expect to start with the number 1, how many with the number 2, and so on? Naively, you might expect 1,111 to start with a 1, 1,111 to start with a 2 and so on. But that’s not what happens in the real world. 1 occurs more often than 2, which occurs more often than 3, and so on.

The Benford’s Law story starts in 1881, when Simon Newcomb, an astronomer, was using some mathematical log tables. For those of you too young to know, these are tables of the logarithms of numbers, very useful in pre-calculator days. Newcomb noticed that the pages for logarithms beginning 1 were more well-thumbed than the other pages, indicating that people were looking for the logarithms of some numbers more than others. Being an academic, he published a paper on it.

In 1938, a physicist called Frank Benford looked at a number of datasets and found the same relationship between the first digits. For example, he looked at the first digit of addresses and found that 1 occurred more frequently than 2, which occurred more frequently than 3 and so on. He didn't just look at addresses, he looked at the first digit of physical constants, the surface area of rivers, and numbers in the Reader's Digest etc. Despite being the second person to discover this relationship, the law is named after him and not Newcomb.

It turns out, we can mathematically describe Benford’s Law as:

P(d) = log(1 + (1/d))

Where d is the numbers 1 to 9 and P(d) is the probability of the number occurring. If we plot it out we get:

This means that for some datasets we expect the first digit to be 1 30.1% of the time, the second digit to be 2 17.6% of the time, 3 to be the first digit 12.5% of the time, etc.

The why of Benford’s Law is much too complex for this blog post. It was only recently (1998) proved by Hill [Hill] and involves digging into the central limit theorem and some very fundamental statistical and probability concepts.

Going back to my accounting example, it would seem all we have to do is plot the distribution for our invoice data and compare it to Benford’s Law. If there’s a difference, then there’s fraud. But the reality is, things are more complex than that.

Benford’s Law doesn’t apply everywhere, there are some conditions:

• The data set must vary over several orders of magnitude (e.g. from 1 to 1,000)
• The data set must have dimensions, or units. For example, Euros, or mm.
• The mean is greater than the median and the skew is positive.

Collins provides a nice overview of how it can be used to detect accounting fraud [Collins]. But Linville [Linville] has poked some practical holes in its use. He conducted an experiment using graduate students to create fake test invoices (this was a research exercise, not an attempt at fraud!) that were mixed in with simulated invoice data. He found that if the fake invoices were less than 10% or so of the total dataset, the deviations from Benford’s Law were too small to be reliably detected.

Benford’s Law actually applies to all digits, not just the first. We can plot out an expected distribution for two digits as I’ve shown below. This has also been used for fraud detection as you might expect.

You can use Benford's Law to detect errors in incoming data. Let's say you have a datafeed of user addresses. You know the house numbers should obey Benford's Law, so you can work out the distribution the data actually has and compare it to the theoretical Benford's Law distribution. If the difference is above some threshold, you can set an alert. Bear in mind, it's not just addresses that follow the law, other properties of a data feed may too. A deviation from Benford"s Law doesn't tell you which particular items are wrong, but you do get a clue about which category, for example,  you might discover items starting with a 2 are too frequent. This is a special case of using the deviation of real data from an expected distribution as an error detection mechanism - a very useful data quality assurance method everyone should be using.

To truly understand Benford’s Law, you’ll need to dig deeply into statistics and possibly number theory, but using it is relatively straightforward. You should be aware it exists and know its limitations - especially if you’re looking for fraud.

# References

[Collins] J. Carlton Collins, “Using Excel and Benford’s Law to detect fraud”, https://www.journalofaccountancy.com/issues/2017/apr/excel-and-benfords-law-to-detect-fraud.html
[Hill] Hill, T. P. "The First Digit Phenomenon." Amer. Sci. 86, 358-363, 1998.
[Linville] “The Problem Of False Negative Results In The Use Of Digit Analysis”, Mark Linville, The Journal of Applied Business Research, Volume 24, Number 1

Wikipedia article https://en.wikipedia.org/wiki/Benford%27s_law
Mathworld article http://mathworld.wolfram.com/BenfordsLaw.html

# Russian novels and business decisions

What has the opening sentence of a 19th-century Russian novel got to do with quantitative business decisions in the 21st century? Read on and I'll tell you what the link is and why you should be aware of it when you're interpreting business data.

# Anna Karenina

The novel is Leo Tolstoy's 'Anna Karenina' and the opening line is: "All happy families are alike; each unhappy family is unhappy in its own way". Here's my take on what this means. For a family to be happy, many conditions have to be met, which means that happy families are all very similar. Many things can lead to unhappiness, either on their own or in combination, which means there's more diversity in unhappy families. So how does this apply to business?

(Leo Tolstoy's family. Do you think they were happy? Image source: Wikimedia Commons. License: Public Domain)

# Survivor bias

The Anna Karenina bias is a form of survivor bias, which is, in turn, a form of selection bias. Survivor bias is the bias introduced by concentrating on the survivors of some selection process and ignoring those that did not. The famous story of Wald and the bombers is, in my view, the best example of survivor bias. If Wald had focused on the surviving bombers, he would have recommended putting armor in the wrong place.

When we look at the survivors of some selection process, they will necessarily be more alike than non-survivors because of the selection process (unhappy families vs. happy families).  Let me give you an example, buying groceries on the web. Imagine a group of people surfing a grocery store. Some won't buy (unhappy families), but some will (happy families). To buy, you have to find an item you want to buy, you have to have the money, you have to want to buy now, and so on. This selection process will give a group of people who are very similar in a number of dimensions - they will exhibit less variability than the non-purchasers.

Some factors will be important to a purchaser's decision and other factors might not be. In the purchaser group, we might expect to see more variation in factors that aren't important to the buying decision and less variation in factors that are. To quote Shugan [Shugan]:

"Moreover, variables exhibiting the highest levels of variance in survivors might be unimportant for survival because all observed levels of those variables have resulted in survival. One implication is a possible inverse correlation between the importance of a variable for survival and the variable’s observed variability"

In the opinion poll world, the Anna Karenina bias rears its ugly head too. Pollsters often use robocalls to try and reach voters. To successfully record an opinion, the call has to go through, it has to be answered, and the person has to respond to the survey questions. This is a selection process. Opinion pollsters try and correct for biases, but sometimes they miss them. If the people who respond to polls exhibit less variability than the general population on some key factor (e.g. education), then the poll may be biased.

In my experience, most forms of B2C data analysis can be viewed as a selection process, and the desired outcomes of most analysis is figuring out the factors that lead to survival (in other words, what made people buy). The Anna Karenina bias warns us that some of the observed factors might be unimportant for survival and gives us a way of trying to understand which factors are relevant.

Leo Tolstoy in 1897. (Image credit: Wikipedia. Public domain image.)

# The takeaways

If you're analyzing business data, here's what to be aware of:

• Don't just focus on the survivors, you need to look at the non-survivors too.
• Survivors will all tend to look the same - there will be less variability among survivors than among non-survivors.
• Survivors may look the same on many factors, only some of which may be relevant.
• The factors that vary the most among survivors might be the least important.

# References

[Shugan] "The Anna Karenina Bias: Which Variables to Observe?", Marketing Science, Vol. 26, No. 2, March–April 2007, pp. 145–148