What fintech can teach retailers about loss

Pedro Ramos is chief revenue officer at Appriss Retail, a data and analytics platform that helps merchants detect fraud, manage returns risk and make real-time transaction decisions.

Returns and shrink are eating retail margins. Most companies know it. Few treat it like the financial risk it is.

Returns and shrink are no longer just operational issues tied to stores or fulfillment. They are financial risks that mirror payment fraud, credit loss, policy abuse and revenue leakage, and they require the same collaborative fintech and retail tech focus applied to those problems.

In 2025, retailers lost an estimated $796 billion to returns and shrink, with $706 billion of that from returns alone. Within that, roughly $100 billion was tied to preventable returns fraud and abuse. That’s a case for solutions that monitor, analyze, and detect patterns across the enterprise.

If loss is big enough to materially impact profit, it needs to register with financial leaders, IT and operations working through visible, unified transaction data that can identify patterns. Fintech solved this problem for payments. Retail hasn't caught up.

Developing a new level of operational and financial discipline

Traditionally, companies address retail loss as a downstream problem. Finance teams review the impact after the fact, while store operations or loss prevention teams attempt to manage it in isolation. That structure worked when transactions were simpler.

Today's omnichannel retailer operates something closer to a payments ecosystem. A single customer can transact across stores, apps, marketplaces, and customer service channels. Fraud and abuse behaviors, as well as operational errors, can be tougher to spot as operational errors blur across these touchpoints. 

The result: decisions get delayed, and loss gets accepted as a cost of doing business.

Organizations that prioritize collaboration, however, are reframing returns and retail loss as enterprise risk, applying data and AI solutions with the same rigor fintech uses for real-time transaction monitoring, customer identity across channels, and risk scoring at the point of decision.

Understanding the cost of returns

Returns generate layered costs: reverse logistics, inspection, repackaging, restocking and liquidation. At each step, margin erodes.

The data problem compounds it. Most returns management solutions are built around a single channel, in-store or online. That means every return decision is being made on incomplete data, and preventable loss goes undetected. A shopper who buys in-store, returns online, and rotates locations looks like three different people inside a siloed, disconnected solution. When that customer's data is unified in one system, it's one pattern that can be addressed. The organization can study consistent customer behaviors and spot patterns.

Most payment providers don’t assess transaction legitimacy using behavioral returns patterns, but with streamlined data across channels, retailers can authorize returns based on consumer history, purchase context and cross-channel behaviors.

Detecting fraud and abuse through data visibility and AI

AI can connect signals across large, messy datasets. But the quality of those decisions depends entirely on the data underneath.

A model trained on one retailer's history sees only a fraction of behavioral reality. The retailers getting the most accurate decisions are those whose AI is built on cross-retailer data, hundreds of millions of consumer profiles, and years of transaction history across in-store, online, and customer service channels.

Using machine learning to reveal transaction patterns, retailers can focus less on how many returns a person makes and more on how their behavior matches known fraud or abuse patterns. The AI can make instant decisions on approving, warning, or declining certain returns to protect margins and the customer experience simultaneously.

Generative AI can be used, too, helping teams investigate transactions to understand the root causes of fraud, abuse, and retail loss across the organization. The tools help retailers address the highest-priority optimizations in policy gaps, training issues or supply chain breakdowns.

Unfortunately, most retailers still have transaction data fragmented across systems: POS, e-commerce, customer service, supply chain, loyalty. Each team sees a slice. No one sees the full picture.

When companies unify that data, patterns become apparent. By tying a return to a customer profile, to a purchase channel, to a fulfillment method, to an inventory discrepancy, AI can help teams diagnose what's happening, prevent further loss, and improve the P&L.

Bringing loss into full focus

Most retailers are running returns and loss management on systems that see only part of the picture — in-store or online, never both. A structural shift helps retailers protect margin by investing in a complete picture of consumer behavior across every touchpoint.

For a retailer with $1 billion in annual revenue, recovering just 10% of preventable returns loss adds tens of millions back to the bottom line. Companies need to treat returns and shrink as the financial risk they are. They need to invest in unified cross-channel data solutions, deploy AI for real-time results and align teams around shared metrics. Those that do will turn loss from a margin drag into a controllable, measurable source of financial performance.