Gambling is rapidly growing as a popular form of entertainment in Latin America and abroad. In this sense, the global gambling market is expected to balloon to USD 516.03 billion by 2026 and the Latin America region will also advance to USD 100 billion by 2024. The thrill of gambling, however, can quickly turn into a problem and cause financial, emotional, and social harm.
In 2022, gambling regulators in the United States, Australia, United Kingdom, Sweden and the Netherlands issued sanctions to numerous online and land-based gambling operators, which included record financial penalties reaching USD 269 million. To avoid these fines, operators have been incentivized to enact responsible gambling measures, such as providing customers with self-exclusion options and implementing tighter age verification procedures.
MORE MOBILE, MORE BETTING
With more people armed with a smartphone to make a bet, the propensity for growth in online gambling is increasing. “We believe that the risks for gambling problems overall have grown 30% from 2018 to 2021, with the risk concentrated among young males 18 to 24 who are sports bettors,” said Keith Whyte, Executive Director of the National Council on Problem Gambling (NCPG) in a 2022 interview.
In today’s world, technology has become a facilitator of problem gambling, primarily due to the convenience of betting from one’s mobile device and advanced technology that can track a gambler’s actions at a granular level. For many operators, it is extremely challenging to separate an individual who places frequent bets but isn’t actually a problem gambler, and a similar player who is at-risk. It’s impossible to manage this problem manually as there is simply too much data to crunch and that approach doesn’t help operators predict a problem, only react to it once it is manifested.
PREDICTIVE MODELS CAN HELP
Predictive models powered by artificial intelligence (AI) have been developed to help with sales prediction, fraud detection, healthcare, and more. In recent years, predictive models have found a new use case: identifying and preventing problem gambling. These models are algorithms that use historical data to make predictions or forecasts about future events or behaviors. Generally speaking, they work by identifying patterns in the data that can be used to make predictions.
Optimove’s responsible gambling predictive model uses the same principle. It employs data analysis techniques to identify patterns and behaviors indicative of problematic gambling, such as sudden changes in betting patterns or an increase in the frequency of playing sessions. By tracking a range of factors, including individual player behavior, demographic information, and game-specific data, the model generates a risk score for each player.
Suppose a player’s risk score exceeds a certain threshold. In that case, the operator can reach out to them and provide resources and support to prevent the development of harmful gambling behaviors. The model’s ability to proactively identify and intervene with at-risk players can help operators promote responsible gambling practices and protect vulnerable individuals. These insights are then used to alter the at-risk player’s marketing messaging diet to a less promotional, more educational one, encouraging them to adopt responsible gambling practices.
HOW THIS SYSTEM IS HELPING OPERATORS
With the predictive model, operators can easily create entire marketing flows, monitor how players migrate from one risk level to another, and adjust their marketing strategies as necessary. These insights empower operators to optimize their marketing strategy and reduce the number of players who become at risk.
Operators can offer recommendations based on individual gambling patterns. For example, an operator may suggest that an at-risk player take a break from gambling after a certain period or set deposit limits to prevent overspending and more. Responsible gambling predictive models stand as an excellent chance to help operators and bettors in Latin America, where gambling is growing rapidly and regulations are often lax to battle this very real problem.