Synthetic Traffic and Bot Farms: Key Features

Atypical Customer Flow and Automated Software App in iGaming

These are serious hidden threats to the entertainment niche. While such phenomena may appear to be regular users, in practice, they destroy the economics of casino products.

About Synthetic Traffic

These are artificially created players or activities that mimic the behaviour of real clients.

The most common types of such a concept are:

• customer flow generated by bots and scripts;
• mass registrations via proxies or VPN services;
• device farms that create hundreds of fake accounts per day;
• “cold” users brought by grey affiliates to earn CPA rewards;
• bonus hunters collecting starting prizes or cashback.

These gamblers appear to be real and interested users. However, in practice, they bring neither income nor value to online casino owners.

How Bot Farms Work

The concept can be described as an organised network of:

• physical devices (phones, tablets);
• virtual machines;
• betting emulators;
• automated scripts.

This system creates thousands of accounts and successfully simulates gaming activity.

Bot farms most often operate as a business project. Therefore, they prioritise massiveness, process automation, as well as the ability to bypass blocks and anti-fraud screens. This is the only way to get a quick profit before entrepreneurs notice fraud.

Reasons for the Spread of Synthetic Traffic and Bot Farms

The owners of entertainment platforms often face low-quality and artificially created customer flow. Such fake accounts do not benefit iGaming portals, but rather force bonuses, free spins, and marketing expenses out of them.

The key reasons for the proliferation of such offences are:

1. Imperfect partner programs. Online casinos increasingly charge for registration or start-up activities on their websites (the deposition of a minimum amount of funds, the launch of a session). This allows grey affiliates to profit from the flow of clients without worrying about its quality.
2. Cheaper cloaking technologies. VPNs, anti-detection browsers, and mobile proxy services are becoming more accessible to the mass audience. Fraudsters no longer have problems connecting such devices and generating dubious traffic.
3. Automation of routine tasks. Bots can register, deposit funds, launch slots, meet minimum LTV requirements, and, of course, withdraw bonus money. Such programs operate so smoothly that even experienced anti-fraud specialists cannot distinguish them from real gamblers.
4. High competition. Affiliates are no longer willing to work hard to attract users because it is much easier to imitate their participation.
5. Product globalisation. Entrepreneurs are increasingly entering regions with weak tracking, minimal KYC requirements, and anti-fraud policies. This also attracts scammers, especially if there is no need to use complex algorithms to simulate the flow of customers on the Internet.

How Synthetic Traffic and Bot Farms Harm Casino Owners

Let us consider the challenges gambling operators face.

False Economy and Distorted LTV

Wrongful approaches form a misleading picture of the entertainment project's success.

For example, LTV (customer lifetime value) seems higher than it actually is. Fake accounts create the illusion of activity, which makes brands believe the attracted audience is really interested in wagering.

As a result, marketing budgets are often wasted. This leads to strategic errors in business processes, from traffic acquisition to product improvements.

Ineffective Remuneration Policy

Bonus hunters and botnets can perform the following tasks:

• create multiple identical accounts;
• take free spins, welcome rewards, and no-deposit incentives;
• make a minimum turnover;
• withdraw bonus money;
• disappear.

This scheme costs operators tens of thousands of dollars in losses. Bargain hunters can never be the source of income: they simply use online casinos to gain a financial advantage.

The budget of gambling platforms is reduced due to:

• direct financial costs for bonuses;
• expenses for transactions;
• payments to affiliates for “new players”.

Entrepreneurs are also forced to tighten their bonus policy because of this; privileges and loyalty points become unavailable to real and solvent customers.

Overpayments to Affiliates

For example, in eCPC and eCPA models, a partner brand receives money simply for clicks or registrations, while bots do this massively and around the clock. Such a cheat leads to an increased amount of money which is sent to affiliates, but the casino's profit remains minimal.

In the RevShare scheme, although businessmen pay for a measurable result, it is created artificially. Because of this, operators spend large sums on attracting and retaining clients who simply do not exist.

Fraud with Payments: Increase in the Number of Cases

Synthetic traffic is closely linked to financial scams:

• the use of stolen bank cards to make deposits;
• money laundering through gaming transactions;
• chargeback attacks, when the card “returns” the payment after a withdrawal.

The consequences for operators can be very different. These include blocking by service providers, increased fees due to risks, additional checks, and penalties from banks.

Distortion of Product Metrics

Synthetic players “break” the analytics of entertainment solutions. iGaming platforms develop according to false KPIs, and funds are spent unreasonably.

What results can raise red flags among entrepreneurs:

• rapid growth of DAU and MAU indicators (this appears to be short-term success, but in reality, casinos are receiving empty traffic);
• a sharp decrease or decline of retention metrics;
• an unnatural extension of the session’s duration and vice versa.

In such situations, the conversion funnel is significantly shifted. Operators mistakenly believe that the initial registration was successful and that they have managed to attract the target audience. After this, LTV metrics drop sharply, as players on the other side of the screen are bots, not real people.

Loss of Licence and Reputation

The world’s leading regulators (UK Gambling Commission, MGA, and others) monitor the following parameters:

• KYC and AML policies of online casinos;
• user behaviour;
• traffic sources, etc.

Mass cheating creates a picture that looks like money laundering or insufficient behaviour monitoring. Entrepreneurs may face official inspections and audits.

If synthetic traffic is detected during analysis, casino owners may be subject to fines or lose their licenses.

Application of Graphs to Combat Atypical Customer Flows

The concept is a “live map” of interactions that evaluates how all these objects are related to each other. Such connections are much more difficult to forge than individual parameters like IP or user agent.

What Data Is Used

Behavioural graphs contain information about the following aspects:

• login devices;
• customer journey on the website and in the mobile app (sessions, clicks, transitions);
• accounts and profiles (registration data, KYC statuses);
• payments (settlement methods, bank cards, transaction identifiers);
• bonus activities;
• traffic sources and affiliate IDs;
• events (logging, bets, withdrawals, refunds).

How Graphs Work

Let us look at how behavioural models function:

1. Data collection and updating. All information generated by the product (logins, transactions, and clicks) is placed in a single stream and standardised.
2. Construction of a graph. Based on the details obtained, basic relationships are formed. Additional metrics are then added, such as the frequency of actions, average check, duration of sessions, and other parameters.
3. Search for anomalies. These are repetitive patterns, atypical structures, broken connections, and other indicators of fraud. Each graph then receives a risk assessment, indicating the likelihood of a bonus network, bot farm, frequent chargebacks, or other violations.

Automatic measures (such as blocking or limiting payments), as well as additional manual verification, can be applied to the detected abnormalities.

Why Graphs Are Effective Against Grey Affiliates and Bots

Let us consider why many modern operators are using behavioural models:

1. Difficulty of network forgery. A bot can change IP, proxy, browser, etc., but cannot imitate complex patterns. These include identical transition chains, repeated payout paths, similar activity time windows, and other metrics that indicate abuse.
2. Analysis of the context. Standard anti-fraud software examines basic characteristics (IP, device, speed), but not the relationships between accounts. Graph models allow casino owners to understand which profiles, for example, use the same devices or get similar bonuses.
3. Early detection of bot farms. Entrepreneurs can instantly block fraudulent schemes, rather than banning thousands of clients individually.
4. Detailed statistics. The special program identifies clear clusters, chains, and atypical unifications. The information received can be used for internal audits and external inspections by regulators.

The Main Things about Graphs in Gambling

Behavioural models are an effective method for combating synthetic traffic and bot farms.

Key aspects that operators should take into account:

• False customer flow and automated software applications cause significant harm to the online casino industry. Business owners spend funds inefficiently, risk losing their licences, and set false strategic goals that are not supported by real metrics.
• The use of graphs simplifies the fight against atypical traffic and bot farms. These models analyse the connection between hundreds and thousands of accounts, instantly identify suspicious activity, and block questionable profiles.

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