The Dutch Gaming Authority (KSA) has used collected (pseudo-anonymised) player data to map indicators of risky gambling behaviour. With the results of the research, the supervisor can compare the various license holders with each other in other areas than just extreme losses.

Gambling problems and excessive gambling are often associated with the loss of large sums of money. Gambling large amounts of money can indeed be an indicator of risky gambling behaviour, but other indicators also play an important role in detecting problem gamblers, such as playing time or changes in gambling behaviour (for example, gambling more often or at unusual times).

Licensed gambling providers have a mandatory data vault (CDB) in which pseudonymised player and transaction data is stored. The KSA has analysed this data to map risky gaming behaviour, to look at (the usefulness of) applied interventions by providers and to compare the different providers with each other.

The study looked at various indicators of problem gambling: intensity (gambling many days, or losing a lot of money), loss of control (number of deposits per day), increase in gambling behaviour (gambling more often or for longer, spending more money), chosen game (for example sports betting versus slot machines) and interventions by the provider. The analyses were conducted based on one year of data from the CDB.

By comparing or linking different indicators, the KSA gained many new and interesting insights. For example, playing at night is generally seen as an important indicator of risky gambling behaviour. The data shows that players play casino games more often than sports bets at night. This could mean that casino games are riskier than sports bets. At the same time, it could mean that placing sports bets at night is a stronger indicator of risky behaviour: it occurs less often and deviates more from the norm.

The KSA is now looking at how this research and the findings from it can be used by the supervisor to gain more insight into possible problem players and how to tackle them. In addition, it is looking at how the insights obtained can be used in other ways to improve supervision.