“Exception reporting” is a term that most retailers have been hearing for some time now. It’s made a huge impact in the North American loss prevention (LP) community and has been saving some retailers hundreds of thousands of dollars each year.
Retailers currently using other, less efficient investigation methods like instore media audits or time-consuming examination of detail or journal tapes are easily sold on the ease-of-use and superior relative efficiency of exception reporting systems.
But retailers have also been hearing about companies spending thousands of dollars in implementation costs. These costs are easily justified for in-place systems because ROI/performance data is readily available. For retailers who are contemplating installing an exception reporting system, estimating value and ROI can be tricky. This is because it’s difficult to predict benefits and ROI when exception reporting systems typically uncover more cases and higher values of fraud than retailers could uncover without the system.
At its most simple definition, exception reporting is a method of data analysis that compares an incoming stream of data to an established base set of data and flags items that don’t quite match up. It’s like the old children’s game in which two pictures are compared and you have to spot what items in the second picture are different from the first.
In the case of POS exception systems, software applications take data from POS systems and compare it to a set of business rules, developed by the loss prevention team, to identify transactions that might indicate fraud or loss. The LP team develops these rules by using existing POS data to define a range in which typical POS transaction fall – average amount of items per purchase, average dollars total per transaction, average returns per day, average dollars per return, and so on. The retailer can define any number of “normal” conditions for the system to compare against incoming data. When POS data feeds into the exception reporting system, it’s compared against this defined range of typical daily transactions. Transactions that fall outside of this range are flagged as suspicious.
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