We simplify fraud and abuse protection for retailers.

Retail is exciting, but it’s also complex and dynamic, which is a challenge. Solutions shouldn’t add to the complexity, they should solve problems in the simplest way possible. Appriss® Retail is focused on solving fraud and abuse problems so our retail customers can focus on serving their consumers and growing the business.

Mitigate risk and loss

Loss prevention teams have to juggle more stores and more types of ever-evolving shrink. We make it easy to keep up.

We help loss prevention teams sift through their data to identify sales reducing activities (SRAs) from consumers, employees, and organized retail crime (ORC). From there, teams can take action to reduce shrink and mitigate loss.

Retail professional using application that enhances her ability to mitigate risk.
two people watch a mild disagreement between a checkout clerk and a male shopper

Track store activities

Lots of things happen in stores every day. We help you keep track of this activity and make sense of it. We leverage generative AI to find patterns and trends in the data so that retail teams can act on it.

Rethink returns

Broad return policies don’t work because customers don’t have the same return habits. By adjusting the way you handle returns, you can proactively fight fraud and retain your loyal customers.

We analyze retailers’ omnichannel transaction data to thoroughly understand which transactions are related to each other—creating a transaction history. With predictive analytics, we determine the fraud risk of each return and authorize, warn, or deny the return in real time.

Retail professionals solving their return policy issues with retail software.

You can save millions with Appriss Retail.

When retailers team up with us, they fight fraud and abuse without sacrificing the customer experience.

Reach out to tell us more about your business, and we’ll create a custom plan and proposal using your real-world data—so you can see just how much you can save.

Ready to start saving?