Signs of problem gambling that may be missed when relying solely on gambling data

As data analytic tools play an increasingly important role in identifying and communicating with at-risk players, it's crucial to understand which signs of problem gambling can be detected through data alone – and, perhaps more importantly, which ones cannot.

If we look at the nine DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) criteria used for diagnosing gambling disorder, only four can be reliably identified using data analytics. 

Criteria detectable with data analytics alone

✔️ Preoccupation
✔️ Tolerance
✔️ Loss of control
✔️ Chasing losses
➖ Withdrawal symptoms
➖ Gambling as an emotional escape
➖ Lying
➖ Work/relationships at risk
➖ Relying on others for financial bailouts

To be diagnosed with mild gambling disorder, an individual must meet at least four or five criteria. This means that when relying solely on gambling data, we are right on the threshold of identifying problem gambling—but with no insight into the other five criteria. And that’s a problem. It means some players may slip under the radar.

So how do we go from pixelated to high-resolution?

Since several risk indicators remain undetectable through gambling data alone, additional information is needed for a more complete and reliable risk assessment. There are several ways to achieve this, and suppliers of responsible gambling solutions have chosen different paths. 

In Preventor we’ve integrated GamTest, a well-researched and validated self-assessment tool where players answer questions about their gambling habits. By combining objective gambling data with self-reported experiences, we get a much clearer picture of a player’s gambling behaviour and risk level. If we revisit the DSM-5 criteria, this combined approach now allows us to detect 8 out of 9 indicators.

Criteria detectable with Preventor

✔️ Preoccupation
✔️ Tolerance
✔️ Loss of control
✔️ Chasing losses
✔️ Withdrawal symptoms
➖ Gambling as an emotional escape
✔️ Lying
✔️ Work/relationships at risk
✔️ Relying on others for financial bailouts

Can we trust self-assessment results?

The short answer is that we can’t be certain—at least not on an individual level. However, GamTest includes multiple statements that contribute to the risk assessment, as well as one key question: "Do you feel that you have had any problems with your gambling?" With over 3 million completed self-assessments, we’ve observed that with increased risk level GamTest’s risk assessment tends to align more strongly with the  players' own evaluations of their gambling habits (the key question above). 

Unlike PGSI, GamTest is specifically designed for online use. Its results are non-linear and include ten times more steps, making it harder to “guess” or manipulate the outcome.

Additionally, self-assessments completed in an unusually short time are classified as less accurate, helping to refine the reliability of the data.

Scientifically sound and validated self-assessment detecting early signs of risky gambling behaviour.

Why do we need a high-resolution risk profile?

For data analytics to have a truly preventive effect, the solution needs to detect early signs of risky gambling behaviour. To achieve this,  we need to take the broadest possible perspective – looking for signs of overconsumption and as many of the 9 indicators of DSM-5 as possible. When we can identify and act on early warning signals, there’s a greater chance that interventions will have the desired effect. 

 The breakdown of risk indicators is also available to the player in the player dashboard. 

Another crucial aspect is effective communication. Limited information makes communication difficult. The more we know about the player, the more likely we are to communicate with them effectively. Depending on the level of risk, this is usually an automated process. But just because it’s automated doesn’t mean it should be generic. 

Rich player analysis – combining gambling behavior with self-reported data – allows Preventor to tailor personalised messages and interactions. These are based not only on the player’s risk level but also on the specific factors driving that risk, and they always include a clear call to action. 

Personalised messages based on risk scoring encouraging action. 

When all is said and done, what really matters is increasing the use of responsible gambling tools. Preventor achieves this by delivering relevant messages to players at moments when they’re most receptive  – even at the earliest signs of risk.