Visualized Correlations

One interesting approach to root cause analysis is to correlate descriptive variables about errors with one another.  I created this correlogram to visualize every possible combination of correlation coefficients among observations from a large information system.  At the intersection of two numbers is a square that represents the correlation of those two variables across hundreds of observations.


Blue shows a positive correlation, red represents a negative, and darker saturation signifies a stronger relationship.  What trends that might give insights to the root causes?  I chose to explore variables 14 (vertical blue trend), 25 (horizontal), and 27 (horizontal).

The analysis was performed in Excel and also in R using the correlogram package.

Using Regression to Predict Duplicate Payments

Recently  used logistic regression on supersamples from 400,000,000 paired invoices in a payment system to identify the factors that best predict if an invoice was submitted more than once.  Some less scrupulous business partners do this in hopes of getting paid twice for the same job.  Positive values in the graph increase the probability of an erroneous payment, negative values decrease that probability, and the width of the line surrounding each point provides a 95% confidence interval that is based on the observations.


I expected the invoice number to be a much larger coefficient but it looks like that number is popular to “fudge” for those that are trying to squeeze an extra payment out of a business partner.  It also looks like questionable invoices are more often submitted at values less than $5K, so businesses aren’t willing to take the same risks on high value invoices.  Is this consistent with what your company has experienced?  Has your company used methods other than logistic regression to get different results?  I’d love to hear about it!