AI can help mitigate, to a point, economic losses from major catastrophes

The land-based gambling industry has been one of the most impacted across the globe due to the ongoing COVID-19 pandemic; therefore, operators have had to come up with fast solutions to compensate for the major changes that this pandemic has brought. After a couple of months of shutdowns, casinos face the need to either implement social distancing measures or be out of operations, which also comes with a challenge to be able to do so while running a profitable business even if operating with a limited capacity. With all the data casinos can collect, artificial intelligence (AI) is becoming a tool that will help business owners to determine the level of impact suffered as well as possible solutions to change that through a thorough analysis. A tech company that specializes in AI-driven optimization of slot machine floors, nQube, is bringing a new proposal to help casinos enhance their slot operations while adapting to the current regulations.
In collaboration with the University of Manitoba Data Science NEXUS, nQube recognizes that casinos need new strategies to not only protect the customers but also to minimize the impact on their financial operations. Among those strategies could be rearranging floors and shutting down some machines to promote physical distance. A careful analysis made to the slot floor will allow the owner to balance many aspects to run the most profitable operation possible, and this is done thanks to AI.
nQube designed software than can analyze the impact that removing or adding a slot machine would add to the casino’s performance and for every machine on the floor. With the help of the partnership with Data Science NEXUS, this software has been modified to apply the same model to help casinos make the best possible decisions when temporarily decreasing the number of active slot machines. “We calculate how limiting the number of slot machines and the maximum allowed casino occupancy will affect slot revenues, and we determine which machines should be left on to maximize revenues,” said Jason Fiege Ph.D., CEO of nQube Data Science.