How to promote responsible gaming through data-driven solutions

Operators can enhance responsible-gaming measures using data collected on platforms.

In brief

• Regulators are setting new standards for responsible-gaming compliance.
• Operators can leverage data analytics and AI algorithms to identify concerning behavior.
• This approach can help operators evolve in a regulatory landscape.

In an effort to address responsible gaming risks, regulators are encouraging operators to inventory the data that they have access to and employ this data to help drive compliance monitoring and create customized data-driven solutions.

Incoming responsible gaming regulations

Land-based casinos and digital operators are teaming with companies to invest in responsible gaming initiatives as an increased focus on monitoring for problem gambling emerges. The onus to identify gaming risk and harmful gaming behaviors is no longer driven by patrons opting to self-exclude.

There are a number of state-specific responsible gaming regulations in place, including self-exclusion, advertising limits and wager limits, credit restrictions and hotlines available to patrons; however, regulators are considering enhancements where operators are to integrate advanced data analytics, setting new standards for responsible gaming compliance in the industry.

Operators are beginning to leverage data analytics and artificial intelligence (AI) algorithms to analyze datasets to identify events consistent with harmful gaming behavior and take action to provide services to those patrons who may be in need.

Integrating AI and data analytics in the gaming industry

Operators have an opportunity to enhance the way they address responsible gaming measures by using the data provided to them. Online gaming platforms currently collect large amounts of data that can be used when conducting responsible gaming analysis:

Using data to assess customer risk

The data can be analyzed to identify patrons who have a higher risk of exhibiting harmful gaming behaviors as compared to those patrons who have elected to self-exclude. The gaming and sports wagering industry has taken data modeling and predictive analytics to new heights; many refer to these measures as an advanced form of customer relationship management (CRM).

CRM systems record data on their patrons and use data modeling to create profiles that help them assess risks for each patron. AI can be used to develop algorithms to detect the traits of patrons demonstrating harmful gaming behaviors and position preventative measures, showcasing a responsible gaming forefront.

For example, in the UK, casino machines use AI to develop a cooling-off system that locks patrons out of the gambling platform when detecting problematic behaviors and promoting safe gambling practices on the screen.

Active measures for operators

Operators should consider having both proactive and reactive measures in place to address responsible gaming. Proactive approaches allow operators to observe changes in patron behavior over time and work to help identify behaviors where preventative measures can be implemented at a much earlier stage in the patron’s lifecycle. Operators should leverage their already existing data and incorporate creative AI/analytical solutions to address responsible gaming issues.

Conversely, reactive approaches allow operators to meet responsible gaming standards on a timely basis for patrons identified as problem gamblers through self-exclusion and limit setting.

Reactive approaches use scenario and rule-driven monitoring to comply with state and other jurisdiction regulations. Scenario-driven monitoring identifies potentially known persons for self-exclusion to ensure those patrons are not able to place wagers, participate in daily fantasy sports contests and play casino games. Queries are run across product options to address outstanding wagers or contests to properly allocate marketing restrictions for certain patrons.

Currently in the UK, operators must take steps to remove the name and details of a self-excluded individual from any marketing database within two days of receiving the completed self-exclusion notification.

Arpi Lal, Alexander Perry, and Elise Lebourg contributed to this article.