Will Gaming Platforms Adopt AI for Customer Risk Profiling?

The gaming industry is growing at an unprecedented rate. Most experts predict that it will be worth $2.2 trillion by the end of 2021. A number of factors are converging to drive the rapid growth in this sector. At the same time, some of these factors are creating new risks that must be monitored carefully.

One of the biggest concerns is the risk of fraudulent customers or losses incurred from unprofitable players. Global gaming brands will need to invest in big data solutions to tackle these problems.

Here are some of the actuarial risks that customers may present to digital gaming brands and the measures that they will need to take to address them.

Mitigating the risk of fraud

Fraud is a very prevalent problem among many gaming platforms. It can manifest in a variety of ways:

People may violate the terms of service by selling their accounts. They may even sell fake accounts to swindle unwitting players. This was a problem a few years ago, when a Reddit user claim that they were selling their Steam account. Other users quickly realized this was a scam.

Some platforms offer customers the opportunity to win money. They may be competing against other players or the house’s online casino platform. With online poker, strategy games simulations and other play for pay systems, they may attract a number of cheaters. Gaming brands must pay close attention to the people that abuse their platforms and try to prevent them from signing up in the first place.

Some people may use these platforms to try to find sensitive information about other players. They can then use this to work around the security questions and other fraud prevention safeguards.

Big data helps these brands assess the threats that individual players may create. They can perform an actuarial analysis before allowing them to create an account. This may entail analyzing:

• The risk profile associated with their region of origin.
• Whether or not their IP addresses associated with any Tor nodes or blacklists that are regularly used by hackers.
• If their names are on any fraud watch lists.
• The type of payment provider they are using to make deposits.
• The number of accounts that have been created from similar IP addresses.

Big data has made all of this possible. Gaming brands can store IP records for 90 days or more. This makes it easier for them to check for users with multiple accounts, which may take in the risk of fraud. They can also access industry data and relevant criminal data to identify possible fraudsters. This process can be automated with new Hadoop tools that have APIs that can connect to public records and industry databases.

Optimizing for highest ROI customers with paid traffic

Most gaming brands have discovered that certain types of customers are going to offer more value over the long term than others. This is especially true with older customers, because they have more money to spend.

Brands must take this into consideration when outlining their marketing strategy. This is especially true for gaming brands with CPA marketing strategies. Since they are paying affiliate marketers for leads rather than sales, they must make sure the customers they reach are going to offer the highest ROI.

According to an expert by Slotsino, a growing number of gaming affiliate programs have teared payments that are adjusted to reflect the value customers of a certain age will bring. For example, IMVU used to be one of the largest gaming brands that I have promoted as an affiliate. They paid $1.85 for subscribers I referred that were between the ages of 18 and 24. They paid five dollars a lead for customers that were over 30 years old. They clearly knew that old are you serious where likely to bring in over 2.5 times more revenue.

Brands clearly leverage big data to assess the value of these users, which helps them decide how much money to spend reaching individual users. They presumably set similar budgets with PPC and other marketing campaigns as well, since most platforms allow them to optimize by user age.