Blog & Cases

In most iGaming products, the recommended games section is generated in one of two ways: either it’s a list of the most popular slots on the platform, or it’s a selection curated manually by the operator’s team. Sometimes it’s a hybrid approach: some games are added manually, while others are selected algorithmically.

This approach is understandable and familiar. But it creates three key problems:

1. All players see the same list, regardless of their preferences.
2. Curated collections require constant manual updates, which is labor-intensive and does not scale well.
3. A static list of games becomes the "default" option rather than a driver of growth.

As a result, the set of recommendations does not consistently help increase retention, LTV, and revenue.

Objective: To increase the operator's revenue and improve the user gaming experience through personalization

To achieve this, MICo (developers of AI and ML solutions for iGaming) integrated the MICo recommendation engine into the product of its client—a gaming operator. The ML model* analyzed each player’s behavior based on a variety of signals and generated personalized game selections.

‍Region:
South Asia

‍Audience:
250,000+ MAU

‍Point of entry:
The “Recommended Games” section, located in the operator’s lobby on the top level; this is one of the key navigation elements that receives a large number of views.
‍*ML (machine learning) — models that analyze data, identify patterns, and make decisions without manual intervention.

The effect was evaluated by comparing two operational scenarios:

1. Manual curation: The list of recommended games is compiled by the operator team.
2. MICo ML Recommendations: The composition and order of games within the same block are determined by an algorithm.

Importantly, the design and layout of the block remained unchanged. No new mechanics were added, and traffic levels remained the same. The only difference was in the logic behind matchmaking.

But to understand exactly what the differences between the two approaches were, it’s worth taking a closer look at how the MICo solution works.

The ML recommender generates a list of personalized recommendations on its own. Here's what happens

• It collects data. The player selects titles, clicks, returns to games, and changes the pace and style—the model analyzes over 200 behavioral parameters for each player.
• It creates a pool of games. Based on the signals, several independent algorithms select relevant games.
• Creates a personalized list. A selection is compiled that reflects the likelihood that the player will be interested in specific games.
• Ranks with a focus on value. The list is reordered to align with the operator’s business objectives—maximizing GGR and ARPU.

Unlike manual curation, which relies on general statistics and past results, ML analyzes the behavior of a specific user and dynamically selects search results, eliminating the need to constantly manually update the list.

Within the first 28 days, the ML-recommendations scenario showed significant growth across key metrics

All metrics were calculated solely for the “Recommended Games” section; in other words, this represents the net effect of the change in the selection logic.

Behavioral metrics:

• Conversion rate from views to clicks: +8 percentage points
• Day-7 retention: +10 p.p.

Financial metrics:

• Average deposit: +40%
• GGR per user in this section: +200%

The results confirm that a recommendation section can be significantly more effective if the selection process is dynamic and based on players' actual behavior and preferences.

What does this mean for operators?

This case study illustrates a simple pattern: recommendations can be more than just a “convenient feature”—they can be a direct driver of revenue if they are adapted to the player’s behavior in real time.

Personalization acts as a real financial lever:

Sustainable growth without increasing your budget. You don’t spend your budget on marketing or hire additional staff—yet your metrics grow because players are getting a more relevant experience.

Reduced manual labor. There’s no need to constantly update lists or allocate resources to do so. The ML solution does this automatically based on real-time data.

Scalability. The more players and events there are, the more accurately the model works. Recommendations can be used everywhere: in the lobby, in search results, in the game list, and even in communications.

The more touchpoints there are, the more noticeable the business impact.

How to Repeat the Result

Test the ML recommender on your own data:

• Duration: 2–4 weeks
• Format: Launch a pilot and evaluate the results
• Result: Measurable uplift and projected growth in metrics

Personalizing recommendations isn’t just a cosmetic improvement. It’s a sustainable growth channel that starts delivering results immediately after launch, without any additional marketing costs. The ML model replaces a static list with dynamic results that adapt to each player, and this has a tangible impact on revenue-related metrics.

VIP players are the small group that drives the platform's unit economics. They typically account for about 80% of any operator's revenue.

That said, the main challenge remains identifying them right from the start. This is critically important, since a high roller’s “lifespan” is estimated at six months, according to various sources; therefore, the sooner such a player comes to the manager’s attention, the more revenue they can generate for the platform.

Why is this opportunity often overlooked? It all comes down to a rule-based approach: the manager opens the CRM, filters the database by deposits, session frequency, and bet sizes—and then relies on their “instinct.”

This model works, but almost always with a delay: a player only comes into focus once most of their life cycle has already passed.

At the same time, simple filters often generate false positives: a player makes deposits for three days in a row, the account manager spends time and bonuses on them, but no conversion occurs.

The MICo team discovered that the necessary signals are already present in the data, but they aren't being detected in time

MICo, a developer of AI and ML solutions for iGaming, analyzed data from a number of operators and found that the behavioral patterns of future high rollers become apparent within the first few days of their activity—long before they can be detected by CRM filters.

However, with the standard approach, such signals usually go unnoticed—they manifest themselves in combinations of dozens of parameters and in the dynamics of a player’s behavior—that is, after the fact.

ML algorithms make it possible to identify such patterns much earlier, and these predictions are more accurate.

Based on this approach, MICo developed the VIP Intelligence ML solution, which identifies high-potential players as early as 3–7 days into their activity and flags them for priority follow-up by the VIP team.

In practice, the ML approach has shown improvement across all key metrics

VIP Intelligence was implemented at a CIS-based operator with an audience of over 700,000 MAUs. The impact was assessed by comparing two scenarios for how managers worked:

• Standard filter — a selection of players with whom managers worked under normal conditions, without ML.
• ML prioritization is a selection process in which algorithms identify players as promising starting on their third day of activity. Managers received these players on a priority list but interacted with them according to the standard procedure.

Key point: Managers did not know whether they were working with players who had been selected manually or as a result of ML prioritization.

Results from the product's first month on the market:

• Players on the priority list reached the VIP threshold in an average of 11 days thanks to ML-based prioritization. Without ML, this figure was about 63 days.
• With the implementation of VIP-Intelligence, the retention rate on the 30th day increased from 20% to 31%.
• After the manager's first contact with the high roller, the player's daily NGR increased by ~30%.

VIP Intelligence analyzes hundreds of signals that cannot be processed manually

The MICo product operates as a SaaS service and can be integrated via an API or another format convenient for the operator. The ML solution begins its analysis as soon as a player logs into the platform.

In the early days, the algorithm processes behavioral signals that cannot be analyzed manually: session patterns, betting dynamics, deposit frequency, reactions to bonus mechanics, and dozens of other variables.

As a result, each player is assigned a "Confidence" score—the probability that the user will show rapid growth and become a high roller. Managers receive a prioritized list sorted by this score.

Result: The team focuses its efforts on those who are most likely to deliver real value.

Early identification of high rollers directly increases the operator's profits

Thanks to this model, players are brought into the VIP department’s spotlight three weeks earlier. This provides an additional, controllable monetization period for each high roller—without increasing acquisition costs.

The MICo solution helps identify who is currently most receptive to communications. Managers don’t waste time on those who aren’t likely to convert. In effect, this boosts the VIP department’s productivity without hiring new staff.

NGR is growing. From the moment a manager first engages with a player, daily net gaming revenue from that predicted player increases by ~30%.

For an operator with 700k+ MAU, this single product alone can help generate tens of thousands of dollars in additional revenue each month. Simply because the platform stops missing out on high rollers during their most active first few weeks.

VIP Radar can not only identify high rollers before manual selection but also assess the likelihood of their churn. This allows you to quickly develop preventive retention strategies and minimize the loss of valuable users.

The case studies address the most common questions from operators

"The ML product simply identifies those who would have brought in a lot of money anyway."

Managers did not know whether they were working with players identified by VIP Intelligence. They treated players the same way both before and after the product was implemented. The differences in deposits, conversion rates, and retention are not due to the players themselves, but rather the result of timely engagement.

"Managers can already see large deposits in the CRM"

They realize it, but it’s too late. A major operator has a user base of tens of thousands of active users, and a manager simply cannot effectively handle more than 100 people a day. 63 days to reach VIP status in a group of hand-selected players—that’s not a theoretical benchmark, but a statistical fact.

"Aggressive attention from the very first week will burn a player out faster."

The data suggests otherwise. Retention among players on the priority list reached 31% on day 30, compared to 20% without ML predictions. Early attention from a manager doesn’t burn out a player; rather, it builds their loyalty to the platform before a competitor does.

ML Prediction Is Becoming the New Standard for Working with VIP Players

The case study shows that an ML solution transforms the economics of working with high rollers not through budgets or bonus policies, but through speed. VIP-Intelligence identifies the right player from among thousands within 3–7 days—something that would be impossible to do manually.

Operators who are implementing ML today have a head start—they identify high rollers before their competitors do and earn more from each customer lifecycle.

In a few years, predictive VIP identification will become a core tool for any operator—just as CRM is today.

MICo is a full-cycle development company specializing in ML-based product solutions, with its own team of ML specialists and infrastructure for data processing and analysis. The company is certified to the international ISO 27001 standard, which attests to its high level of data security and protection.

AI/ML (Machine Learning) casino software covers several use cases: early VIP detection, churn prediction, Next Best Action, and personalized game recommendations for better player engagement.

In this guide, we’ll explain how each use case can increase iGaming business revenue and share tips on how to choose an AI software that fits your workflow.

What is AI casino prediction software?

AI casino prediction software is a gaming analytics tool that uses predictive algorithms to forecast player behavior. It evaluates dozens of signals, including activity patterns, deposits, bet dynamics, bonus response, and session frequency. As new behavioral data appears, the model updates each player’s potential and churn risk scores.

MICO’s Player Intelligence starts prioritizing player’s potential from the first days of activity.

AI prediction tools shouldn’t be confused with software that manipulates RNGs, odds, payouts, or gameplay. Instead, they provide CRM and VIP teams with predictive insights. Managers receive a ranked list of players who need attention before player activity and value decline.

What AI prediction software does

Algorithms in casino software perform four retention tasks:

• Score players by future value.
• Flag early churn risk.
• Recommend the next best action.
• Rank games by predicted player interest.

Player Intelligence models analyze signals such as deposits, session frequency, bet dynamics, and bonus response. Recommendation engines also use lobby and in-game behavior, including clicks, title choices, returns to specific mechanics, session pace, and play style to build a dynamic feed with multiple games for a personalized experience.

How AI models learn from player data

AI models learn from historical data: player behavior during the first sessions, return frequency, changes in deposits, player preferences, and later outcomes, such as VIP conversion or churn. The model then compares new activity with these patterns and updates its predictions as more events appear. It can estimate both the likelihood of VIP conversion or churn and the player’s interest in specific games.

Key features to look for in AI casino prediction software

Ask four questions when comparing AI casino prediction tools:

1. What outcome does the model predict?
2. Which data does it use?
3. How does it explain the score?
4. Where does the output enter the existing workflow?

These questions translate into the following features to check before choosing a tool for your iGaming business.

Early behavioral signal detection

The solution should detect shorter sessions, slower return rhythm, changing bet dynamics, bonus fatigue, longer gaps between deposits, and weaker response to manager outreach. Сombining sequential activity and aggregated player data improves prediction over using either type alone. The output should also show the changes in pattern, confidence score, along with a list of players who need attention first.

Early VIP identification

An Early Detect model should identify high-value potential before standard CRMs flag the player. In this case, VIP managers receive clear priority lists of potential VIPs earlier, which gives them more time to contact players and build relationships while their value is still growing.

MICo’s Player Intelligence starts prioritizing potential VIPs on the first days of activity.

Churn prediction

A false positive means the team spends bonus budget and manager time on a player who was unlikely to leave. A false negative can cost more in VIP retention: if the model fails to flag a high-value player in time, the team will have fewer chances to protect LTV.

Reliable prediction software should let operators evaluate model errors by business cost. The model should provide clear confidence levels, measurable precision and recall, and a workflow that prioritizes cases for CRM or VIP teams.

Player’s potential

A player value model can identify which players are worth investing in and which have reached their growth potential. This gives teams clear priorities, helping them maximize the impact of every interaction while reducing time spent on players with limited future value.

Optimization strategy

An uplift layer can  turn potential or churn risk into a right communication with users:

• What offer to use.
• When to send it.
• Which channel to choose.
• Whether the player needs manager contact.

Personalized game recommendations

A Machine Learning recommendation engine should replace one static game list with a dynamic feed for each player. It uses clicks, title choices, search history, returns to game mechanics, session pace, and play style to predict interest across multiple games.

Depending on the product, the engine may use collaborative filtering, content-based logic, or a hybrid model. It forms a pool of candidate titles, combines game properties with player preferences and session context, and ranks the final selection around operator goals.

Integration with CRM and operator infrastructure

VIP and potential scores, churn risk, and game recommendations should fit into the existing CRM or VIP workflow without manual exports or extra sorting. A recommendation engine returns ranked title lists to the lobby, search, or another product section.

For smooth integration, iGaming operators need to confirm the required event data, player-ID mapping, delivery format, update frequency, and where each output will appear.

AI casino prediction software for iGaming operators in 2026

AI casino prediction software in the iGaming industry solves different retention problems.

MICo AI

MICo AI best suits operators that need early detection of VIP players and measurable retention impact. Player Intelligence analyzes dozens of behavioral signals, assigns each player a Confidence score, and gives teams a ranked priority list.

In a 700k+ MAU case, ML-prioritized players reached the VIP threshold in 11 days instead of about 63. Day-30 retention rose from 20% to 31%, and daily NGR after the first manager contact increased by about 30%.

The software works as a SaaS and integrates through an API or another operator-friendly format.

What to check before choosing an AI/ML prediction tool

Because the platforms solve different problems, start by defining the prediction target. Decide what the AI casino prediction model should do and improve:

• early VIP detection, 
• churn-risk prioritization,
• identification of player potential
• manager outreach,
• or the next retention action.

Once the target is clear, assess data quality. The model needs stable player IDs, clean event tracking, enough historical data, and consistent definitions of deposits, sessions, churn, and VIP status. CRM and VIP teams should also see the confidence level, key signal changes, and reasons to act on a player now.

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Finally, check how the model fits the retention workflow. It should be compatible with the existing CRM, teams, and data stack without manual sorting. 

Book a demo and see what your platform can achieve.

Over the last couple of years, the term "AI" has become the go-to answer to any question about technology. An algorithm selects adverts — AI. A chatbot answers questions — AI. A service recommends a film, a game or a bonus — that’s AI too.

The fact is that this term covers a range of different technologies, each with its own limitations, implementation costs and outcomes. Let’s sort out the differences once and for all.

It is important not to confuse the two: AI is not the name of a single specific technology, but rather a whole class of approaches.

Spoiler: a neural network is one of the tools used in machine learning, and machine learning is a branch of artificial intelligence.

What is AI and how does it work?

Artificial intelligence (AI) is a general term for systems that perform tasks that typically require human involvement: recognising patterns, making predictions, taking decisions, interpreting data or interacting with users.

It sounds broad — because it really is a broad concept. Netflix, which knows that on a Friday evening you want to watch a long sci-fi film starring Actor X on your Smart TV, and on a weekday morning you want to watch a short stand-up routine by the same actor on your smartphone — that’s already AI. The developer didn’t programme the system to know this, but the system identified this connection on its own from millions of micro-interactions specifically with you, and now predicts your choices more accurately than you could articulate them yourself.

To avoid getting confused by the terminology, picture a kitchen.

The kitchen is fully equipped with all the appliances and fittings.

• Machine learning (ML) is one way of cooking. It’s the most popular method today, but it’s not the only one.
• Neural networks are one specific approach within machine learning. They’re trendy and powerful, but not a one-size-fits-all solution. You’ve no doubt come across this in articles discussing which neural network is best at generating content, which is best at generating images, and so on.

In short, it looks like this:

AI → ML → neural networks → deep learning

Why does everyone think that AI = a neural network? And what exactly is a neural network?

ChatGPT, Midjourney, Sora — every publication is writing about them. Yet an ML model that predicts player churn or assesses credit risk operates "behind the scenes", because we don’t have to make any effort (in the form of creating a prompt) to obtain the right content.

This is where the confusion lies: people see the eye-catching interfaces and the impressive results produced by even the simplest query, and start to believe that AI consists entirely of these elements.

In practice, this is not the case.

In a large number of applied problems, it is not generative models that provide the greatest value, but rather more specialised forecasting and optimisation systems.

But let’s take a closer look at what this is all about. At its core lies an idea inspired by the structure of the human brain. Only, instead of a multitude of biological neurons, it uses artificial ones, organised into layers.

Each of these "neurons" receives data, processes it and passes it on through the network. During processing, the neural network identifies patterns in the input data and produces an output — for example, generated text, an image or music.

A few layers make up a neural network. A large number of layers constitute deep learning (DL). It’s the same idea, just on a different scale. This is precisely where ChatGPT and Midjourney come into their own.

Popular neural networks such as ChatGPT and DeepSeek can handle everyday, straightforward tasks involving text, images and patterns — but this is not enough for business tasks.

So what actually works, then? Let’s have a look.

ML — what it is and when it’s needed

Let’s talk about our industry — iGaming. It’s easy to confuse automation, analytics and machine learning in this field.

Where ML can help drive business growth

• personalised game recommendations;
• player churn prediction;
• assessment of the likelihood of a deposit or repeat session;
• prioritisation of players for retention;
• dynamic offer selection;
• identification of complex behavioural anomalies.

The principle is simple. You provide the model with examples: a history of player behaviour, session results, and deposit data. It looks for patterns — what do those who have left have in common? What distinguishes a future high roller from ordinary users as early as the first week? Once it has identified these patterns, the system memorises them. And when new data comes in, it makes a prediction. There’s no magic involved — just maths.

It might seem that with this approach, you could delegate all the work to a "machine", but ML isn’t always necessary. There are tasks that can be solved perfectly well using fixed rules — there’s no need to invent anything. Here’s a simple test: if a task can be solved using an if-else rule, ML is unnecessary here.

Where a "hands-on approach" is often sufficient

• a bonus upon reaching a set level;
• blocking of bets above the limit;
• a fixed "first deposit ×2" promotion;
• newsletters sent according to a pre-set schedule;
• a simple list of popular games ranked by number of plays.

Other types of ML — and when they perform better than neural networks

A neural network is a powerful tool, but it is not a one-size-fits-all solution. When it comes to working with structured data, it is often other classes of ML models, rather than neural networks, that prove most effective.

The most popular of the classical algorithms are ensemble methods — where several models "vote" on the answer: Gradient Boosting and Random Forest. In iGaming industry applications, these often outperform neural networks in terms of accuracy whilst being less costly.

Classic ML algorithms. They work much like an experienced analyst using a table: they examine the features, weigh them up and produce an answer. In typical application scenarios, they often strike the best balance between data requirements, cost of use, interpretability and model accuracy.

However, for a number of problems, neural network methods are indispensable. In situations where the problem involves unstructured data or complex representations:

• text;
• images;
• speech;
• audio;
• video;
• content generation.

When it comes to recognising, interpreting or creating such content, neural networks are usually the obvious choice.

Choosing a method is not a matter of fashion. It is a question of the task at hand, the quality of the data, and how important it is to explain why the model arrived at a particular decision.

How this works in the real world

There are usually two images of AI in people’s minds.

AI from the films — it thinks, reasons, and is almost human. ChatGPT, which writes poetry and engages in conversation. It’s beautiful and impressive, but it’s not suitable for most business tasks.

AI from the real business world — it takes your data and says: "Players with three or more sessions a day will leave within two weeks if they don’t receive a bonus during their first session after a break. Right now, we need to offer a bonus to these players, but not to those ones".

Here at MICo, as developers of ML solutions for iGaming, we focus on the latter. This is because iGaming operators need predictable, interpretable ML — a tool whose impact can be measured in monetary terms.

The focus here is on answers to specific questions:

• which players need to be retained right now;
• who can be identified early on as potentially valuable;
• where the marketing budget is being spent ineffectively;
• which segments require different communication approaches;
• which actions have the greatest impact on revenue and retention.