Issue 7
While flashy and dramatic stories about catching fraudsters perk up ears, it doesn’t translate to funding.
An investigative systems expert at a large retailer has been pitching controls on receipted returns for months. Business leaders who hear “return fraud and abuse” picture organized theft rings meticulously plotting heists from the shadows.
They’re not picturing Jim who overestimated how effective his new diet was and bought three sizes and returned two.
But of the 14% of total fraud and abuse, the teams and systems that get renewed every budget cycle without question only target for 2% (explicit fraud) of that total. The other, non-glamorous 12% (Jim and friends) that’s being targeted by other teams and systems? They go overlooked.
But first, your industry brain teaser of the week:
How much more likely are frequent returners to engage in bracketing behavior compared to occasional returners?
Scroll to the bottom for the answer.
What's in stock
Here’s what we have in store for you this week:
- The Rundown: One retailers approach to building AP teams that influence budgets
- Worth Your Time: Agentic AI is overhyped, store fulfillment is expanding, and federal ORC legislation advances.
- What We’re Up To: NRF Protect and June Takeback Talks
The Rundown
The investigative systems expert we spoke to has seen how the theft case with a drone gets executive buzz while the database fix that stops a million dollars in policy-exploiting returns barely gets a nod or a blink.
Both examples tell important stories about protection, but the way they translate doesn’t always land. Finance leaders don’t speak fraud and abuse metrics. Their world is margin impact. And the person who understands and can explain what the systems detect usually isn’t in the room when the budget conversation happens.
When their team does win approval though, they lead with numbers: “This unmanaged risk is costing us $X annually.” Not: “We need this tool to stop return fraud and abuse.”
They explain the margin leak before pitching the fix. They make sure someone who can speak both languages is actually in the meeting.
Four shifts for reframing include:
- Lead with the business risk
- Know the outcome of each loss
- Identify who actually makes the pitch
- Build cross-functional relationships before the crisis
On top of that, their teams build relationships with Product Management and IT before a crisis hits, so when RFID gets deployed or AI governance gets scoped, AP has a seat at the table instead of finding out after decisions are made.
As retailers are moving more of their budgets toward AI, AP teams using tools that flag abuse patterns and protect margin need to position that work as AI investment. If they don’t, it’ll be seen as legacy software that gets cut to fund someone else’s technology roadmap.
You don’t have to win in every room, just the ones where the check gets signed.
Worth your time
We know time is money, so we won’t waste yours
- Agentic shopping is getting buzz, but actual consumer adoption remains low. Why the hype is running ahead of reality (Retail Brew)
- New research shows in-store fulfillment of online orders is expected to rise significantly, putting more pressure on store operations (Chain Store Age)
- The House passed the Combating Organized Retail Crime Act, moving federal ORC legislation one step closer to law (Loss Prevention Media)
What we're up to
We headed to Grapevine for NRF Protect, June 8-10.
Monday evening we took a group through downtown Grapevine for dinner, wine pairings, and a nightcap at Cross Timbers Winery. Small group, great night.
Tuesday we were on stage with Tim Murfin from Winn-Dixie and our Chief Product & AI Officer, Vishal Patel, walking through how Total Retail Loss turns exception-based reporting into enterprise action in our session, The Grocery Proving Ground.
All week, we ran complimentary Returns Health Assessments—benchmarking preventable returns loss against pooled data from our customer base. Most people were surprised by the number.
Takeback Talks returned June 25—VP+ only—on The AI Penthouse Problem: your returns AI has three layers, and most teams are only working in the top one.
Want to know where we’ll be next? See our upcoming events.
