Detecting fraud in e-commerce orders with Benford's law

Benford's law is a popular empirical law that states that the first digits of a population of data will follow a specific logarithmic distribution.

This law was observed by Frank Benford around 1938 and since then has gained increasing popularity as a way to detect anomalous alterations in a population of data.

Basically, testing a population against Benford's law means verifying that the given population respects this law. If deviations are discovered, the law performs further analysis for items related to those deviations.

In this recipe, we will test a population of e-commerce orders against the law, focusing on items deviating from the expected distribution.

Getting ready

This recipe will use functions from the well-documented benford.analysis package by Carlos Cinelli.

We therefore need to install and load this package:

install.packages("benford.analysis")
library(benford.analysis)

In our example, we will use a data frame that stores...