Responsible Gambling Technology: How AI Is Changing the Way Operators Identify At-Risk Players

Artificial intelligence is rapidly becoming the centrepiece of operator responsible gambling (RG) programmes, with a growing number of tools now capable of identifying patterns of potentially problematic gambling behaviour with significantly greater precision than the rule-based systems they are replacing.

The leading platforms — including Kindred's own Playscan system, NEC's gambling harm detection AI, and third-party solutions from GamCare and GREO — use machine learning models trained on anonymised historical player data to identify behavioural markers associated with problem gambling: rapidly increasing session duration, unusually high deposit frequencies, changes in game selection patterns, and increases in bet sizes relative to historical norms.

Operators that have deployed AI-powered RG tools report measurable improvements in early intervention rates. One major UK-licensed operator, which asked not to be identified, told iGaming Pulse that its AI-powered system identified at-risk players an average of 18 days earlier than its previous rule-based system, and that player acceptance of RG interventions increased by 23% when interventions were contextually timed by the AI rather than triggered by fixed thresholds.

However, the increasing use of AI in responsible gambling has also raised ethical questions. Consumer advocates and academics have raised concerns about transparency — operators are generally not required to disclose to players that they are being assessed by algorithmic systems — and about the potential for model bias if training data over-represents certain demographic profiles of problem gamblers.

The UKGC has indicated it will address AI governance in responsible gambling as part of its White Paper implementation, with new operator requirements for algorithmic accountability expected to be consulted on in Q3 2026.