How to Detect Fraudulent Chargebacks in the iGaming Industry

Chargeback fraud is a growing pressure point for iGaming operators. Whether it’s a player reversing a legitimate loss or a fraudster using stolen card details, the outcome is the same: lost revenue, mounting fees and strained fraud teams. But chargebacks don’t happen in isolation. They’re often the final step in a broader abuse pattern involving fake accounts, bonus exploitation and identity fraud.

In this article, we unpack why chargebacks are particularly damaging in the iGaming space and how smarter detection can help you stop them before they start.

Why Are Chargebacks a Problem for the iGaming Industry?

In iGaming, chargebacks are more than just a financial nuisance; they’re a threat to your operating model. As a high-risk industry in the eyes of card networks, iGaming is already under heightened scrutiny. Add in fraudsters exploiting promotions, stolen payment methods and friendly fraud from opportunistic players, and the risk compounds quickly.

Here’s why unchecked chargebacks can be so damaging:

• Every chargeback comes with fees: Whether the transaction was legitimate or not, operators are on the hook for admin fees triggered by the card network’s liability shift.
• Disputes drain time and resources: Fighting a chargeback requires evidence, detailed logs, and a team that understands complex card scheme policies. It’s rarely a quick win.
• High ratios mean higher costs: Exceed the acceptable chargeback threshold, and card processors can raise your fees or, worse, classify your business as too risky to support.
• You risk losing access to card payments: In extreme cases, repeated violations can result in suspension from major card networks, forcing a reliance on less preferred or riskier payment methods.

On top of that, there’s the growing issue of players intentionally claiming their transaction was unauthorized after making a deposit and playing. These cases are harder to prove, easier to abuse and increasingly common.

Yes, chargeback representment is possible. But winning a dispute means more than showing a receipt. It requires airtight data and a deep understanding of network rules. And even then, the process itself can erode trust and margins.

How Do You Detect Chargebacks in the iGaming Industry?

Chargebacks rarely happen in isolation. They’re often the final step in a broader pattern of fraudulent behavior, and that pattern typically begins long before the refund request. To stop chargebacks before they impact their bottom line, operators need early signals, enriched data and a fraud strategy built for flexibility. Here’s how to detect chargeback fraud more effectively:

• Strengthen digital onboarding: Fraudulent chargebacks often follow fraudulent signups. Strengthening onboarding flows allows operators to identify risky users before deposits are made or bonuses are claimed. Consider screening for behavioral anomalies, inconsistent identity data or suspicious device and location signals.
• Enrich signup and payment data: A player’s card and personal details can reveal critical risk indicators. Performing BIN lookups and combining that with digital footprint analysis (such as examining email address validity, IP geolocation and device data) helps build a complete user profile. The data is also essential when contesting a chargeback through representment.
• Apply flexible risk rules: Chargeback risk varies across verticals, regions and even promotional campaigns. One-size-fits-all fraud rules often lead to false positives. Customizable, modular risk scoring allows operators to finetune detection strategies and avoid unnecessary friction for legitimate players.
• Use machine learning to uncover patterns: Machine learning can help identify complex correlations that manual reviews miss. For example, it can highlight whether players sourced from a particular affiliate or those who claim bonuses immediately upon signup are more likely to initiate chargebacks. These insights can then be transformed into custom, confidence-scored rules that reflect your platform’s specific risk environment.

Top 3 Custom Risk Rules to Reduce Chargebacks in iGaming

While no single rule can prevent all chargebacks, well-calibrated, data-driven signals can help identify risky behavior early and trigger appropriate actions before losses occur. Below are three custom rule examples that can strengthen your fraud prevention strategy without disrupting the player experience.

1. Mismatch Between Card Issuing Country and IP Location

A common signal of potentially fraudulent behavior is a geographic mismatch between the user’s IP address and the country of their card issuer. This can be identified through a card BIN lookup, which helps verify whether the card’s origin aligns with the player’s apparent location. For instance, a card issued in the US being used from a high-risk or unrelated region could warrant further scrutiny.

While this doesn’t guarantee fraud, flagging these cases for manual review enables risk teams to verify details before any chargeback risk escalates, especially at the point of deposit or large-value transactions.

2. Sudden Spike in Deposit Activity

A rapid increase in deposit volume — such as a 200%+ jump within 24 hours — may suggest account abuse, particularly when linked to stolen payment credentials. Fraudsters often test a card with smaller bets before attempting to extract as much value as possible in a short timeframe.

Rather than immediately blocking the user, this behavior can incrementally raise their risk score. When combined with other signals, it supports more confident decision-making, whether that means account monitoring, applying dynamic friction or triggering a temporary freeze.

3. Lack of Digital Footprint or Identity Signals

Players who register using contact details (such as email addresses or phone numbers) that show no presence across digital channels may be using disposable or fake credentials. While this alone doesn’t confirm malicious intent, it’s a useful indicator when building a fuller risk profile.

By checking for the presence of social media, messaging apps or domain reputation associated with a user’s contact details via digital footprint analysis, operators can assess the likelihood of the account being legitimate, especially when combined with behavioral and device data.

How SEON Helps iGaming with Fraudulent Chargebacks

SEON enables iGaming operators to prevent chargebacks and related fraud,  including bonus abuse, multi-accounting and identity manipulation, without compromising the player experience. By turning basic data points like an email address, phone number, card or device specifications into enriched insights via digital footprint analysis and device intelligence, SEON builds a complete user profile from the very first interaction. This includes 200+ social and digital signals that go far beyond traditional fraud checks.

Operators can automate approvals, reviews and declines using transparent risk scoring tailored to their platform. Each decision is explainable, with clear visibility into which rules were triggered and why. With full control over custom rule creation and logic, risk teams can adapt quickly to evolving fraud patterns without needing to overhaul their systems.

SEON’s machine learning engine continuously learns from your historical outcomes, surfacing patterns and threats specific to your user base, all while reducing false positives and streamlining operational workflows. Designed with flexibility in mind, SEON integrates easily into any iGaming tech stack via modular APIs, allowing teams to deploy it as a standalone fraud solution or layer it seamlessly onto their existing tools.