Retail fraud is a growing problem that is costing retailers billions. According to the National Retail Federation (NRF) 8% of total retail sales (over $260 billion) are returned and 3% (over $9 billion) of those returns are fraudulent.
Fraud can take many faces in the retail industry including the return of stolen merchandise, return of merchandise purchased with a fraudulent tender, misuse of gift cards, and chargebacks. Chargebacks occur when a consumer flags a credit card transaction as fraudulent and the consumer is issued a refund through the bank. Fees are associated with chargebacks and some retailers pay north of 4% of their monthly sales in chargebacks along. Fraud creates real & unnecessary bottom line costs that may not be obvious on the surface.
While retail fraud may seem like a daunting problem, similar to fraud in the banking industry there are telltale signs of potential fraud to focus in on. In the banking world cash deposits over a certain threshold ($10,000) may need to be reported to a government agency, in an effort to fight money laundering. Criminals work around this by making frequent deposits of cash just under the $10,000 reporting threshold. Likewise in the retail industry NRF estimates 10% of all returns without a receipt are fraudulent, while less than 1% of purchases made online and returned to stores are suspected to be fraudulent. In both industries the keys to unlock the problem exist, and the answers are in the data.
So if the answers to fraud problems are in the data, how would a retailer uncover fraud to stop the problem and protect the bottom line? The answer is a data visualizationplatform which equips the business and IT to do the following:
With the holiday season on the horizon, retailers all over the world can be better prepared to uncover fraud and protect profits. Billions of dollars are at stake, and with discounting and promotions fueling sales across the holiday season a retailer can earn back precious margin points by minimizing or eliminating fraud. I invite you to take Qlikfor a spin to see how you can see the whole story in your data.
Image credit: Jeffrey Edmonds, Flickr