Forensic Accounting: Cohort Analysis

I continue my series on forensic accounting techniques with cohort analysis:

In the last post, we focused on high-level aggregates to gain a basic understanding of our data. We saw some suspicious results but couldn’t say much more than “This looks weird” due to our level of aggregation. In this post, I want to dig into data at a lower level of detail. My working conception is the cohort, a broad-based comparison of data sliced by some business-relevant or analysis-relevant component.

Those familiar with Kimball-style data warehousing already understand where I’m going with this. In the basic analysis, we essentially look at fact data with a little bit of disaggregation, such as looking at data by year. In this analysis, we introduce dimensions (sort of) and slice our data by dimensions.

Click through for some fraud-finding fun.

Related Posts

Reinforcement Learning with R

Holger von Jouanne-Diedrich takes us through concepts in reinforcement learning: At the core this can be stated as the problem a gambler has who wants to play a one-armed bandit: if there are several machines with different winning probabilities (a so-called multi-armed bandit problem) the question the gambler faces is: which machine to play? He could “exploit” one […]

Read More

Refreshing Views After DDL Changes

Eduardo Pivaral shows how you can refresh the metadata for a view in SQL Server after one of its underlying tables or functions changes: So we proceed to execute an alter view over the first view: ALTER VIEW dbo.[vi_invoices_received_by]ASSELECT ConfirmedReceivedBy as [Received by], COUNT(InvoiceID) as [# of Invoices], CustomerIDFROM Sales.InvoicesGROUP BY ConfirmedReceivedBy, CustomerID;GO So we […]

Read More


April 2019
« Mar May »