Over the past two years, the term “AI” has become extremely popular in the betting industry. Almost every platform proudly claims, “We use AI, so we are smarter and more advanced.” However, the reality is often disappointing. Many platforms invest heavily in AI, yet do not generate meaningful profit from it.

Why?
Because AI is not a magic wand. It is simply a tool. Whether it makes money depends entirely on how you use it.

According to Sigma World’s analysis,
AI creates value in betting mainly in three areas: risk management, customer support efficiency, and personalized recommendations.

At the same time, platforms that misuse AI, exaggerate its capabilities, or fail to understand it can easily move in the wrong direction.

This article explains, in straightforward terms, the three core values AI brings to betting platforms.

1. AI as the “Security Guard” — Helping You Prevent Losses

The original purpose of bringing AI into betting platforms was simple: to reduce errors, control risks, and make operations safer and more stable.

Example: Identifying Bonus Abusers

Previously, a bonus-hunting group might register 100 accounts to exploit your first-deposit bonuses. Manual review was exhausting and often missed many fake accounts.

How AI manages this:

AI does not rely on usernames at all. It analyses devices, IP connections, and betting patterns. Even if someone uses 100 different identities, AI can instantly detect the link and report:

“These 100 accounts originate from the same user. Please block them.”

Sigma’s report states:
“AI is a core technology for improving security and behaviour recognition in betting platforms.”
— Sigma World: AI in Betting — Protection or Marketing

In the past, these tasks required heavy manual judgment and were prone to error.
Today, AI can perform these checks automatically, making risk control faster and far more accurate.

In this category, AI’s value lies in strengthening platform safety.

2. AI as the “High-Efficiency Customer Support” — Reducing Operational Burden

As platforms expand and introduce more features, manual operations quickly become overwhelmed.
AI’s role is to handle repetitive tasks and significantly increase operational efficiency.

AI customer support and automated workflows are among the most effective examples.

Sigma notes:
“AI customer support not only reduces manpower cost, but also improves response speed and player experience.”
— Sigma World: AI, Customer Support and iGaming Revenue

How AI enhances operations:

Example: Deposit failures

Previously, when a player’s deposit failed, the player might simply close the website and leave. By the time a human support agent reached out, the player had already moved to another platform.

How AI handles this:

The moment a deposit fails, an AI message appears:

“It seems the payment channel is congested. Please try this alternative channel. We will add a 1% bonus immediately.”

Example: PWA Push Notifications

Similar to the PWA push notifications developed by Kzing, operators no longer need to manually send messages.
When a monitored match begins, AI automatically notifies players who follow similar events:

“Your favourite team has started the match. Join now.”

To operators, AI saves time, manpower, and operational costs.
To players, AI provides instant responses and a smoother experience.

This reflects AI’s two-way value.

3. AI as the “Revenue Engine” — Driving Strong Platform Growth

This is the most challenging area, but it is also where the largest competitive advantage emerges.

The key concept is prediction.
The goal is not to react after an event occurs, but to anticipate what a player will do next.

Example: Predicting Player Churn

Traditional operation:
Operators only notice that VIP players are gone after they stop logging in, then urgently attempt to contact them.

AI-driven operation:
AI detects that your VIP player, “Old Wang,” has reduced his login frequency and appears frustrated after a recent loss. AI predicts:

“There is an 85% probability this player will leave.”

Automatic intervention:
The system immediately triggers a personalised action, such as sending a “luck recovery bonus” or having VIP support reach out before the player makes the decision to leave.

Example: Personalised Game and Content Recommendations

Traditional operation:
Send the same EPL Manchester United match to all players.

AI-driven operation:
AI understands:

• Player A prefers Live Casino 
• Player B prefers obscure high-odds league matches 

Therefore:

• Player A sees baccarat and live dealer games on the homepage 
• Player B sees small-market football matches with high payout potential 

Personalised content naturally results in higher conversion rates.

💡 Three Practical Recommendations for Operators

If you believe the insights above are useful and want to implement AI, please read these suggestions before making large investments:

1. Poor data produces poor AI results

AI is entirely dependent on the data it receives.
If your player data (tags, transaction history, game preferences) is disorganised or incomplete, then AI will produce inaccurate or even harmful outputs.

Recommendation:
Do not immediately invest in expensive AI models.
Your first step should be to clean and structure your player data.
Strong data foundations allow AI to produce valuable results.

2. Use AI to retain players first, not to acquire new ones

Traffic has become extremely costly. Acquiring a single new player can be more expensive than gold.

Do not expect AI to solve your user acquisition challenges first.
You should use AI to retain existing players and increase their lifetime value (LTV).

Recommendation:
Focus on conversion and retention tools such as:

• Kzing’s in-platform chatroom 
• Progressive check-in rewards 
• PWA push notifications 

Retaining one active player is far more cost-effective than spending ten times more to acquire a new one.

3. Building an in-house AI team is slow, expensive, and unnecessarily difficult

The effectiveness of AI depends heavily on the underlying system.

The fastest path is to work with a provider like Kzing, whose backend already includes:

• Flexible progressive check-in reward systems 
• Advanced scheduled and targeted push capabilities 
• A stable, safe, and scalable backend designed to support AI-assisted operations 

There is no need to develop complex code internally.
You can use ready-made tools to maximise value and reduce both time and cost.

AI Is Your “Magnifier”

AI will not turn a weak platform into a strong one.
However, it will amplify the strengths of an already well-structured platform, making it several times more competitive.

In the future, there will only be two types of betting platforms:

1. Platforms that use AI to generate profit 
2. Platforms that are defeated by competitors who use AI 

The best time to begin is now.

📞 Interested in using ready-built tools to implement AI-driven strategy?

Please contact the Kzing Sales Team:

Derek Cheong 小波
📧 derek.cheong@kzing.com
📱 +63 928 699 8616
📨 Telegram: @KzingSales03

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