Play Sessions Under Control: How Casinos Detect Signs of Fraud

Modern iGaming platforms analyse not only payments, devices, and IP addresses, but also the sequence of user actions on the website. This analysis provides a valuable data set on habits, transitions, and hidden associations between accounts.

Why Gaming Sessions Need Systems That Fight Scams

For the average person, such entertainment looks quite simple. A client enters the digital portal, launches slot machines, places bets, participates in promotions, and, if necessary, withdraws a prize.

For an antifraud service, each session represents a unique data set. It can be used to evaluate customer behaviour and identify potential risks.

During the gameplay, the system tracks:

• which slots (cards, arcades) people launch;
• how often they place bets;
• how the size of deposits changes;
• how actively bonuses are activated;
• how long a session lasts;
• how quickly a gambler withdraws funds;
• what actions are performed before and after placing a wager.

This information helps create a behavioural profile for each visitor to the platform. Over time, the system begins to understand the normal playing style for a selected account and which decisions appear unusual.

It is also worth noting that many fraudulent schemes manifest themselves through customer actions. Even if an attacker uses a VPN, changes devices, or creates new profiles, their performance within the casino remains similar.

Modern anti-fraud platforms analyse not only individual events but also the entire chain of steps taken by gamblers. Algorithms evaluate how much time passed between registration and the first deposit, which slots were used, how actively bonuses were applied, and how the session developed in general.

Such careful examination allows operators to create the most accurate digital portrait of their clients, where every new click, bid, or launch of rounds becomes an additional signal to the anti-fraud system. The more such data is collected, the faster the algorithms distinguish between regular clients and fraudsters.

Analysis of Behavioural Patterns

Each player shapes a unique style of interaction with online casinos over time.

Gamblers develop:

• personal preferences;
• familiar bet sizes;
• typical duration of sessions;
• individual reactions to wins, losses, and bonus offers.

For example, for one player, short daily sessions of 15–20 minutes may be normal, while for another, long games once a week are better.

The anti-fraud platform accumulates such information and creates a behavioural profile for each client. Based on historical data, the system determines the normal activity range and immediately detects any deviations from the familiar pattern.

Potential signs of risks include:

• sudden changes in bet sizes;
• atypical activity in odd hours;
• unusually long sessions;
• transition to new products without an obvious reason;
• excessive use of bonuses;
• a sharp growth in the volume of games after a long absence.

It is important to understand that none of these signs alone is evidence of fraud. However, a combination of several anomalies can increase the customer’s risk profile and become the reason for a more detailed analysis.

Bot and Automated Game Detection

The main problem for operators is that the actions of a program can appear quite plausible. To disguise themselves, fraudsters use scripts, specialised software, and fake accounts that easily fulfil various wagering requirements.

A deeper analysis reveals that automated behaviour differs from that of a real person.

To identify such schemes, anti-scam systems estimate the following parameters:

• intervals between bets;
• speed ​​of response to events;
• regularity of actions;
• sequence of gaming operations;
• nature of interaction with the interface;
• frequency and repeatability of the chosen scenarios.

Unlike bots, actual users always act irregularly. They take breaks of varying duration, change the game speed, and occasionally depart from their standard script. Too consistent steps or engagement in the same activities, on the contrary, almost always raise the anti-fraud services’ suspicions.

Modern algorithms analyse:

• interface navigation speed;
• nature of transitions between sections;
• how natural the interaction with website elements looks.

If the system detects signs of machine activity, a profile may be sent for additional verification or even blocked.

Search for Multiple Accounts

Analysis of customer behaviour within the platform becomes the basis for identifying multiple accounts.

Even if gamblers try to hide the connection between their profiles, their habits often remain the same. A person can register on the digital portal many times, use different devices and payment methods, and still play nearly identically.

For anti-fraud algorithms, such coincidences become a valuable source of information.

The system compares dozens of characteristics, including:

• favourite titles and game categories;
• average bet size;
• frequency and duration of sessions;
• activity time during the day;
• what happens after registration;
• ways of using bonuses;
• wagering requirements implementation scenarios;
• website navigation features.

For example, several profiles that are formally unrelated may appear on the platform. They all use different devices, settlement methods, and registration details.

However, when they launch the same slots, place similar bets, and apply for prize withdrawals almost simultaneously, this is a potential sign of multi-accounting.

Situations in which many pages exhibit virtually identical behaviour patterns for an extended period are considered quite suspicious. The more such coincidences the system detects, the higher the likelihood that the profiles belong to the same person or a group of related users.

Analysis of Gaming Sessions Before Withdrawing Funds

Cashing out bonuses and rewards is one of the most important stages that helps detect fraud.

The behaviour of an ordinary person usually looks quite natural and diverse.

After making a deposit, clients launch various titles, adjust bet sizes, explore platform offers, utilise bonuses, and spend time on the gaming site. Their activity develops gradually and is not limited only to withdrawals.

However, scammers often act differently. Their main goal is to fulfil the required conditions as quickly as possible and gain access to payouts. Anti-fraud programs carefully analyse the entire customer journey, which allows them to accurately identify offenders.

Situations where a visitor shows almost no interest in the gameplay and immediately focuses on withdrawals might be suspicious. Users may make a deposit, place the minimum bets to claim the incentive, and request their prizes.

Such scenarios often occur in the following situations:

• bonus abuse;
• multi-accounting;
• usage of someone else's payment details;
• money laundering schemes, and other types of financial fraud.

To protect themselves against such a crime, most casinos perform additional inspections before processing payouts. If the system detects unusual behavioural patterns or a high risk level, the request may be suspended for further analysis.

In some cases, clients are asked to undergo additional verification or provide additional documents.

The Role of Artificial Intelligence

Unlike traditional rules, which operate under predefined scenarios, AI can find more complex linkages between events. Algorithms analyse thousands of parameters simultaneously and evaluate not only the individual steps gamblers take but also the big picture.

The system can take into account:

• the speed of various actions;
• payment activity;
• technical details of the devices;
• connection geography;
• results of previous checks, and many other factors.

Based on the information obtained, artificial intelligence forms a comprehensive assessment of the user behaviour and identifies signs of fraud that may go unnoticed during the initial analysis.

Modern AI models make it possible to:

• detect hidden cases of multi-accounting;
• discover new bonus abuse schemes;
• recognise automated activity;
• predict the likelihood of a scam even before a financial transaction is made.

It is also worth noting that artificial intelligence-based algorithms are constantly learning.

If attackers decide to use new methods to bypass protection, the system quickly adapts to the changed conditions and improves its risk assessment models. This allows anti-fraud services to be more effective even without specialist intervention.

The Main Things about Play Session Analysis

Key aspects that operators should take into account:

• Specialised software detects bots and automated gaming, finds cases of multi-accounting, and evaluates sessions before the funds are withdrawn. This allows the program to identify fraudulent activity almost unmistakably.
• The implementation of artificial intelligence helps entrepreneurs collect huge amounts of information, quickly systematise it, and detect criminal schemes. Furthermore, AI programs are self-learning and can update their risk models.

The 2WinPower studio provides advanced solutions for protecting gambling platforms.