Responsible gaming in the age of machine learning

The problem with waiting

Traditional responsible gaming relies on self-exclusion, deposit limits and complaints. By the time those trigger, harm has usually already happened.

The data already holds the signals

Every deposit, bet and session says something about how a player is behaving. The challenge is decoding it at scale, for every player, every day.

How ML helps

Real-time anomaly detection and data mining can predict high-risk players during play. A common approach is a risk score: when a player crosses a threshold, they receive a personalised message and a self-reflection test.

Typical model inputs include:

• Lifetime deposit, plus deposits over the last 7, 15 and 30 days.
• Minimum, maximum and average deposit size.
• Equivalent measures for bets and wins.
• Changes in play time, stake size and limit settings.

Responsible tools players can use

• Time outs and permanent account closure.
• Reality checks that show how long a player has been playing.
• Session time limits and deposit limits.
• Credit card restrictions, as required in the UK.

Intervening the right way

The goal is to communicate with players in an intelligent, proactive and targeted way. Operators who can show a consistent, data-driven process also earn more trust from regulators.