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Fraud has long been a key issue in the industry, with significant costs throughout the ecosystem. Why is this still such a hard problem to solve?
It’s not that operators can’t spot suspicious activity anymore. Most fraud teams have got a lot better at that over the years. The real problem is deciding what to actually do once something is flagged. At GeoLocs, we see this challenge regularly through both fraud and geolocation-related risk signals. Detecting an anomaly is one thing, deciding on the right response is often much harder.
Multi-accounting, location spoofing, bonus abuse and device manipulation are not new issues. What has changed is the sophistication and speed of the tactics, and the commercial pressure operators are under to onboard players quickly. That combination puts risk teams in a genuinely difficult spot. If you act too slowly, fraud eats into revenue and undermines trust in the platform. Act too aggressively and you start blocking or over-verifying legitimate players for behaviour that was entirely innocent. That ultimately is where the approaches need refining.
Where do you see operators getting this balance wrong most often?
The core mistake is treating every risk signal as equally serious. Some signals, such as a rooted or jailbroken device, may warrant immediate action, such as an auto-block. Others require additional context before a decision is made.
For example, a location anomaly could be caused by a VPN, travel, inconsistent network data or another legitimate factor. In these cases, with a tool like Fraud Shield, operators can choose to issue a warning, request additional verification or simply continue monitoring the account. Similarly, multiple accounts on the same device may be legitimate family members using a shared tablet or computer, rather than an organised bonus-abuse ring.
Having worked with geolocation data in regulated gaming since 2012, we’ve seen how important context is when distinguishing genuine fraud from normal player behaviour. When fraud teams respond to every flagged signal in the same way, such as auto-blocking, that’s when legitimate players start getting caught in the crossfire. That’s where the player experience suffers, trust erodes and valuable customers risk being lost unnecessarily.
What does a better approach look like in practice?
It means matching the response to the severity of the risk, the strength of the evidence, and the operator’s own commercial priorities, rather than a single blunt model. That’s the thinking behind GeoLocs’ Fraud Shield. Built specifically for regulated gambling, it builds on the location, device and risk intelligence GeoLocs already provides to give operators the flexibility to make better risk decisions.
Operators can define and configure their own risk thresholds and response rules, so Fraud Shield automatically applies the right action based on player behaviour and fraud signals. Whether that’s monitoring an unusual location pattern, issuing a warning for suspected spoofing activity, applying a temporary restriction, escalating for manual review, or a full block. The decision always stays with the operator.
This reduces reliance on manual review, lets risk be monitored in real time rather than retrospectively, gives fraud teams stronger evidence to work with and allows them to combine geolocation insights with wider fraud indicators to make more informed decisions. The result is better detection accuracy, faster case resolution and fewer false positives, all while still supporting regulatory compliance and protecting the experience of legitimate players.
It’s not reinventing anything. Banks and payment providers have run tiered fraud responses with contextual evidence and human judgement layered over automation for years. Gambling operators are increasingly expected to operate with that same discipline, especially with regulators scrutinising fraud exposure and player protection in the same breath.
Does one risk model work for every type of operator?
No, and that’s part of the problem with a lot of fraud tools currently on the market. They’re built around one fixed response curve. What operators actually need is the flexibility to apply different responses based on context. Tools like Fraud Shield give operators the ability to decide in advance which signals, including location, device and behavioural indicators, trigger which response, based on their own risk appetite, regulatory environment and commercial goals. A sportsbook running aggressive acquisition campaigns in a new market has a completely different risk tolerance to an established casino brand focused on retention. A one-size-fits-all model serves neither well.
What separates the operators who will stay ahead of fraud as tactics keep evolving?
The future of fraud prevention isn’t more alerts or stricter controls. It’s giving operators the context they need to separate genuine threats from legitimate player behaviour. As fraud tactics evolve, competitive advantage will come from having a clearer picture of player risk, drawing on location, device and behavioural intelligence to make faster, more informed decisions.
