Refund abuse is emerging as a critical AML compliance challenge for regulated firms

Fraudulent returns are becoming an AML compliance issue

In recent years, regulators have come to believe that financial sector firms must not limit themselves to individual analysis of payment disputes or refunds. They are now obliged to investigate the signals these activities transmit about wider financial crime risks.

ZIGRAM, a RegTech company specializing in anti‑money‑laundering (AML) solutions and the fight against financial crime for banks, FinTechs and payment processors, maintains that these patterns should be integrated into full AML monitoring, not viewed merely as operational losses caused by fraud. The main problem is that fraud teams and AML teams often examine different parts of the same actions. While fraud specialists focus on individual disputes or suspicious refunds, AML departments track unusual movements of funds. When these functions operate independently, links between seemingly unrelated events can remain unnoticed.

A customer may make purchases at multiple merchants, request refunds through different channels, dispute transactions with their bank, and transfer funds between payment instruments, without any single event automatically triggering an AML investigation. This is where the concept of fraud‑AML convergence, known as FRAML, comes in. ZIGRAM argues that regulated organizations must combine fraud indicators with transaction monitoring to form a clearer picture of customer behaviour and identify patterns that would otherwise remain fragmented.

The financial impact of fraudulent returns is significant. In 2023, the total cost for retailers exceeded 101 billion dollars, with approximately 14 percent of returns classified as fraudulent. In addition, companies suffered an average loss of 13.70 dollars for every 100 dollars of returned goods. In the same year, 34 percent of merchants worldwide acknowledged „friendly fraud” as one of the main threats in e‑commerce.

For compliance teams, the major challenge lies in determining when repeated refund or dispute activity becomes more than simple fraud and turns into a potential financial crime issue. ZIGRAM highlights several indicators: high‑frequency refunds coming from multiple merchants, all directed to the same account or prepaid card; refunds sent to a payment instrument different from the original one, without clear explanation; and cross‑border refunds involving high‑risk jurisdictions. Other warning signs include the rapid transfer of returned funds to cryptocurrency exchanges or third‑party accounts, simultaneous use of multiple customer profiles sharing devices or delivery addresses, signs of account takeover, and attempts to manipulate customer service staff to process refunds. Merchants with dispute rates significantly above industry averages may also require additional scrutiny.

Overall, these behaviours may reflect the three stages of money laundering: placement (introducing illicit value), layering (obscuring movement), and integration (reintroducing funds into the legitimate economy). Fraudulent purchases can introduce illicit value, repetitive refunds and transfers can conceal the flow, and resale of goods or conversion into other assets can help integrate profits into the legal economic system.

The financial impact of fraudulent returns is

ZIGRAM cites a case in the United States, in which over 111 million dollars were transacted through shell companies that manipulate dispute rates to keep acquiring accounts open, while fraudulent activity continued. This example shows how payment fraud can exceed individual transactions and involve extensive networks of entities and accounts.

Traditional AML monitoring systems have been developed mainly on the basis of signals such as cash thresholds, structuring and cross‑border transfers. ZIGRAM contends that these systems must now include signals generated by refunds and disputes. The proposed methodology combines consolidated transaction and refund data with behavioural baselines, velocity analysis and entity resolution. The framework also includes creating rules that take into account refund frequency, transaction values, cross‑border activity and changes in payment instruments. Alerts can then be routed between fraud and AML teams according to risk level, and investigation outcomes can be fed back into detection models and system rules.

Customer‑level risk factors can offer an additional layer of context. PEP status, sanctions exposure, negative media and prior fraud activity can be evaluated together with refund and dispute behaviour to decide whether a pattern warrants further investigation.

ZIGRAM has launched several products to support this integrated approach to financial crime. Transact Comply integrates refund and dispute activity into transaction monitoring. Entity Hero offers relationship mapping between customers, merchants and payment instruments. Dragnet Alpha adds negative media information, while PreScreening.io and DueDiliger support enhanced screening and due diligence before onboarding.

By combining these tools and adopting a holistic perspective on refunds, companies not only reduce financial losses but also strengthen their compliance position in the face of AML regulations.