Neural Networks in iGaming: How AI Segments and Retains Players

The entertainment industry is undergoing rapid transformation under the influence of technology, with artificial intelligence emerging as a key driver of these changes.

Integration of Neural Networks in the Sector

When working with a client base, modern operators face 2 key tasks:

1. Detection and suppression of the bonus hunters’ activities. These are players who use rewards only to enrich themselves, often through multi-accounting and bypassing restrictions.
2. Accurate segmentation of casino visitors. Dividing people into groups facilitates the personalisation of content and increases retention indicators, as well as the income of entrepreneurs. The risks and life cycle of each consumer are taken into account.

The integration of neural networks and machine learning algorithms allows operators to perform both tasks with a high degree of precision and automation.

Who Are Bonus Hunters

These are gamblers who register on iGaming sites only to benefit from the following promotions:

• welcome free spins;
• rewards for the first deposit;
• cashback programs;
• temporary offers and promo codes.

Such users do not play long-term. Their goal is to quickly receive gifts, minimise risks, withdraw funds, and leave. These gamblers often create multiple accounts and use VPNs, proxy masks, or automated scripts.

Bonus hunters are a real problem for modern online casinos, and there are several reasons for this.

Financial Losses

Incentives are part of the marketing budget designed to attract and retain a loyal audience. Their inefficient spending leads to an increase in the number of false registrations and low-quality leads, as well as the worsening of the marketing campaigns’ profitability (ROI).

Operators simply lose money without receiving active users in return.

Multi-Accounting and Bypassing Restrictions

To re-use bonuses, fraudsters create several profiles. They also use the following tools:

• fake documents and e-mail;
• different devices, proxies, and VPN;
• stolen or temporary payment methods.

This violates the rules of fair play and creates risks of licensing problems (especially in countries with strict control over KYC/AML).

Vulnerability for Regulators

Gambling commissions (especially in the EU and the UK) require casino owners to:

• combat fraud;
• protect the budget from abuse;
• supervision of traffic sources and purity of the database.

The mass presence of bonus hunters leads to huge fines, penalties, cancellation of licenses, and violation of partnership agreements.

Distortion of Analytics and Behavioural Data

Bonus hunters are a “noise” in the information. They twist engagement and retention metrics, mislead automated recommendation systems, and ruin segmentation or A/B tests.

Marketers cannot adequately assess what works and what does not because they see “parasitic” players in the reports rather than loyal ones.

Load on Operations Teams

Each suspicious account:

• requires additional KYC verification;
• can initiate disputes, chargebacks, and complaints;
• increases the load on the technical support and compliance department.

This is an additional cost for entrepreneurs, especially in a highly competitive industry.

Detection of Bonus Hunters Using Neural Networks

According to Sumsub:

• in 2024, 69.9% of identified frauds in the iGaming niche were related to the theft of incentives;
• systems based on neuronets allow operators to reduce losses from abuses by 30–50% in the first months after the connection.

How Neural Networks Identify Bonus Hunters

Such solutions (ML models in particular) are trained on a large dataset:

• historical behaviour of players;
• actions within the system;
• technical parameters of devices and sessions;
• transactions;
• deviations from the norm.

Key features that the tool analyses:

1. Financial activity. Suspicious signals for the AI ​​system are making a minimum deposit and instant withdrawal of funds.
2. Gaming style. Most often, this is a lack of interest in actual playing, for example, using only small bets.
3. Repeatability of actions. The artificial intelligence-based model records the moment when the same steps are taken in different accounts.
4. Technical data. The most common tactic of fraudsters is the use of one IP address for several profiles. Bonus hunters also apply proxy servers and VPN services to hide their geolocation and circumvent the rules.
5. Temporal patterns. Suspicions may be aroused by playing immediately after activating a promotional offer during the so-called “bonus hours”.

How the AI ​​Model Works in Practice

Let us consider the main stages of identifying scammers:

• Data collection. The behaviour of gamblers is logged in real-time. The emphasis is placed on sessions, transactions, bets, and devices used.
• Model training. For this, a historical database is used, tags indicating who the bonus hunter was.
• Anomaly detection. At this stage, deviations from the behaviour of ordinary casino visitors are recorded. This can be the launch of slots with minimal volatility (90% of the time), bets in the exact amount to wager the incentive, immediate withdrawal of funds after that, and other patterns.
• Scoring and reactions. Based on the collected and processed information, clients are assigned a “risk score” (for example, from 0 to 1). If the value is high, the neural network automatically blocks access to new rewards, activates manual account verification, and introduces additional restrictions (KYC and cashout limits).

Examples of Technologies and Tools

Let us consider what components are used to identify bonus hunters:

1. Deep Learning. LSTM-type neural networks can analyse the sequence of player actions (session by session).
2. Random Forest and Gradient Boosting. Such solutions are used in anti-fraud systems. The key function is a binary classification (division into regular customers and scammers).
3. Graph ML. These models help detect multi-accounting through the interconnection of IP addresses, devices, and settlement methods.

The use of a wide range of tools provides:

1. Flexibility. Neural networks can quickly adapt to new fraud schemes, instantly responding to changing patterns.
2. Accuracy. Artificial intelligence takes into account hundreds of features simultaneously and produces more reliable scoring.
3. Scalability. The system works in real-time and evaluates the flow of players 24/7.
4. Transparency of decisions. Modern models make it possible to explain the reasons for blocking, which is useful for KYC. It also protects operators from unjustified claims.

AI-Based Audience Segmentation

The ​​program automatically splits gamblers into behavioural groups using machine learning. Unlike classical division (by gender, age, or country), artificial intelligence focuses on the real actions of customers within the system.

AI models are trained based on the following data:

• betting history (frequency, size, and preferences for slots and card games);
• activity (days and hours of play, the duration of sessions);
• financial behaviour (deposits, withdrawals, and bonuses);
• decisions within bonus programs;
• devices, geolocation, and entry channels;
• KYC profile and interaction with client support.

How a Neural Network Divides Users into Groups

Let us consider the main stages of segmentation:

1. Collection and purification of information. For this, a dataset is created, which records details on all active and former players.
2. Model training. Several algorithms are considered basic here. These are clustering (k-means, DBSCAN, and HDBSCAN), Autoencoder, and PCA protocols for compression or extraction of hidden features, deep neural networks, and gradient boosting for behavioural classification.
3. Formation of the segments. Artificial intelligence automatically groups casino visitors by the type of their actions, loyalty, churn rate, reactions to bonuses or promotions, as well as the probability of problematic gaming and other features.
4. Implementation of results. The data obtained allows entrepreneurs to quickly personalise offers, effectively manage risks, automatically limit incentives, or increase limits. The system sends notifications and provides individualised support at the right time (for example, a message with the suggestion of a break).

Examples of Behavioural Segments

Groups of customers in the entertainment sector:

1. High rollers. Such users bet large amounts of money and play consistently. It is possible to influence them by offering exclusive promotions and VIP bonuses.
2. Beginners. They have recently registered on the website and, as a rule, have minimal experience. When interacting with them, it is best to provide training sessions and welcome bonuses.
3. Profit seekers. This category is active only during incentives and seasonal promotions. To interest such clients, it is better to reduce free rolls and send additional KYC requests.
4. Gamblers at risk of leaving. Such customers gradually decrease their activity in online casinos and respond less often to rewards and special offers. To retain them, business owners can apply re-marketing and personal cashback.
5. Problematic users. They are characterised by too frequent and chaotic play, as well as a sharp transition from medium to high-low bids. In this case, an effective solution will be blocking bonuses and activating Responsible Gambling mechanisms.

How Market Leaders Use Neural Networks

Let us consider real cases of top casino providers.

Kindred Group: AI Clustering

The owner of Unibet, 32Red, and other brands processes a huge array of data — about 3 billion transactions per day. These are bets, withdrawals, gaming sessions, IP addresses, devices, bonus activity, and interaction with technical support.

To extract valuable information from this flow, the brand implemented AI clustering.

Dynamic segments of clients are created based on their actions and lifecycle:

1. The potential of the value. How much gamblers are willing to spend and what is their significance in the long term.
2. Behavioural patterns. These are preferences for games, sizes of bids, and the duration of sessions.
3. Stage of the life cycle. The company identifies newly registered customers, clients at risk of outflow, and those who have successfully passed reactivation.
4. Perils. These are a high frequency of betting, sharp activity jumps, responsible gaming labels, and other patterns.

The effects of introducing AI clustering:

• 24% rise in audience retention;
• 80% increase in response (CTR) to targeted promos.

Kindred Group's marketing ROI has also grown as the brand’s personalised offers work much better.

Bet365: AI Platform for Segmentation

In 2022, the company partnered with Optimove, a leading CRM service, to optimise player retention and LTV.

How the system works:

• software based on artificial intelligence integrates with the Customer Data Platform (CDP), collecting historical and real-time details: transactions, payments, bets, and user behaviour;
• the best next action for each gambler is determined (e-mail, push, or bonus offers) without manually setting up campaigns;
• AI evaluates deposit potential, churn rate, optimal communication time, etc.

The result of implementing such platforms is:

• the ability to simultaneously deploy hundreds of personalised campaigns across thousands of segments;
• increased marketing efficiency, LTV, and audience engagement.

Bet365 was one of the first providers to demonstrate a data-driven approach: artificial intelligence independently selects the optimal channel and message for each segment. The use of manual labour is minimised.

888 Casino: AI Recommendations and a Virtual Assistant

The built-in module analyses the behaviour of gamblers in real time (favourite entertainment, duration of sessions, metadata, etc.) and offers them the most relevant content (roulette, slots, and baccarat).

888 Casino applies a content-based filtering approach: players see a special selection of products immediately after authorisation.

In 2023, the company also launched the Amanda virtual assistant, which operates based on NLP.

The neural network processes user requests (withdrawal time, bonus conditions) and makes warnings in relation to Responsible Gaming. The AI ​​program refers to key phrases that require the intervention of specialists.

The results of the implementation of the assistant:

• an increase in the speed of request processing by 15–25%;
• a reduction in the load on technical support;
• early detection of problematic behaviour.

The Main Things about Neural Networks and AI in Gambling

These concepts are becoming an integral part of the operational strategy of entertainment companies.

Key aspects that entrepreneurs should take into account:

• The integration of technology is a strategic tool for protecting the profits and reputation of casino brands. Neural networks help prevent budget leaks for bonuses, clear the user base of fake accounts, as well as improve analytics and marketing. Thanks to this, the work of business owners complies with regulatory requirements, and the load on technical support is reduced several times.
• The segmentation of players based on AI and neural networks is a powerful tool in the arsenal of entertainment firms. It allows operators to understand the behaviour of different types of customers, predict their actions, and create personalised offers.

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