Multi-accounting fraud is one of the significant concerns for most consumer-facing online businesses operating in 2024,negatively impacting revenue and brand reputation. From gaming and gambling to fintech and e-commerce, fraudsters exploit multiple accounts for personal gains.
This article goes in-depth into multi-accounting, its repercussions on other industries, and how it works. More importantly, it offers actionable new strategies to combat this continuous threat.
What is Multi-Accounting?
Multi-accounting refers to a situation where a user has registered several accounts on a platform or service. This happens mostly to abuse promotions, or manipulate systems for personal gains.
Most common tactics that fall under multi-accounting include using different email addresses or phone numbers, virtual private networks—VPNs—to mask IP addresses, using many devices or emulators, and creating accounts with slight changes in personal information.
Types of fraud in multi-accounting
Here are the most common types of frauds in multi-accounting:
Promo Abuse
A user creates several accounts to manipulate promotional offers, sign-up bonuses, or referral rewards multiple times.
How Promo Abuse Fraud Works
Scammers register for promotional offers through numerous email addresses and phone numbers by using diverse details like name, address or location discounts or promotions several times. This is done by employing VPNs, and device emulators as well.
Fake Reviews
A person uses multiple accounts to write fake positive reviews for their own products or negative reviews for competitors. Its purpose is to tamper with ratings of goods and services on various platforms.
How Fake Reviews Fraud Works
Impersonators use fake accounts to write favorable comments on their products or bad reviews on those belonging to others. They frequently change their names, locations, and gadgets to make it look like they are not the same person. Using this technique, they boost the search ranking artificially to mislead the customers. This tactic can also be used to destroy the reputation of a company as well.
Affiliate Fraud
Fraudsters create numerous accounts aimed at producing false clicks or sign-ups to further manipulate affiliate marketing programs.
How Affiliate Fraud Works
The usual way of doing affiliate frauds is generating fake traffic, stuffing cookies, injecting clicks and imitating domains. By doing this, they attract more clicks and sales, though the dealers are not really interested resulting in a huge loss on the parts of business entities.
To avoid affiliate fraud, organizations can apply traffic pattern analysis, use fraud detection tools or collaborate with good affiliate networks in order to keep their marketing intact.
Bonus abuse in Gaming
Fraudsters create multiple accounts to avail of online casino and sportsbook welcome bonuses and other promo offers.
How Bonus Abuse Works
To create fake accounts, fraudsters employ all sorts of tricks, such as:
- Combining genuine and fictitious details to make synthetic identities
- Using VPNs or TOR browsers in order to hide IP addresses and look like users from other location
- Using bots, virtual machines, emulators, and fake residential IPs as means of generating more accounts
Consequences of Multi-Accounting Fraud
Multi-accounting fraud can have serious consequences for online businesses:
- Financial losses
Companies suffer real monetary losses through undeserved payouts, bonuses, and rewards made to fraudulent accounts, defeating the purpose of promotionals offers.
- Brand reputation damage
Uncontrolled multi-accounting may start mistrust in the minds of real users and cause a loss in the reputation of fairness and security that companies may have.
- Impact on genuine users
This could mean that either stricter verification methods are put in place for the legitimate customer or companies limit promotion access because of the fear of fraud.
Methods of Multi Accounting Fraud Detection
There are multiple methods of identifying multi accounting frauds:
| Detection Method | Description | How It Works | Limitations |
| IP Address Tracking | This involves monitoring IP addresses used to create and access accounts. | – Log IP addresses during account registration and logins. – Analyze geographical locations for unusual patterns (e.g., multiple accounts from the same IP). | Users may share IP addresses (e.g., public Wi-Fi), necessitating additional verification methods. |
| Device Fingerprinting | This refers to collecting unique device characteristics to identify users. | – Gather device-specific data during account creation and login. – Use algorithms to compare fingerprints of new accounts with existing ones. | Users can change device settings or use different devices, complicating detection. |
| Behavioral Analysis | This analyzes user behavior patterns to identify anomalies. | – Monitor metrics like session duration, transaction history, and interaction patterns. – Use statistical models to detect deviations from normal behavior. | Sophisticated users may mimic normal behavior, making detection challenging. |
| Email Analysis | Email analysis involves examining email addresses for signs of fraud. | – Filter known disposable email domains. – Look for patterns in email addresses that suggest they belong to the same user. | Legitimate users may also use similar email patterns, requiring additional context for interpretation. |
| Biometric Verification | The process uses biological characteristics to authenticate users. | – Require biometric data during account creation or verification. – Use biometric data to check for links between multiple accounts. | Privacy concerns and the need for specialized hardware can limit adoption. |
| Machine Learning and AI | It involves analyzing data to identify patterns and anomalies. | – Train models on historical data to recognize typical user behavior. – Deploy models for real-time monitoring to flag suspicious activities. | Requires significant data and expertise; models need regular updates to remain effective. |
| Cross-Referencing Data | It’s about comparing various data points to identify links between accounts. | – Collect and store relevant data points for all accounts. – Use algorithms to identify connections based on shared data attributes. | Legitimate reasons for shared data (e.g., family members) can complicate interpretations. |
| User Reporting | This encourages users to report suspicious activity. | – Provide tools for users to report suspected fraud. – Consider offering rewards for valid reports of fraud. | Relies on user vigilance and may not capture all instances of fraud. |
Tools and Solutions For Handling Multi-accounting Fraud
Multi accounting has become a matter of concern for B2C brands. However, with some practical solutions, the menace can be contained to a great extent:
1. Advanced User Verification
Implementing robust verification processes, like two-factor authentication (2FA) and using biometric data can make it more difficult for fraudsters to scam companies. This adds an extra layer of security for companies.
2. Behavioral Analytics
Multi-accounting fraud is detected through behavioral analysis or machine learning algorithms that check for rapid creation of accounts, strange purchases and logins from different locations. AI algorithms can analyze user behavior patterns across accounts to identify similarities that may indicate multi-accounting.
AI models use multiple factors including login times, device usage, typing patterns, and other behavioral biometrics. They can also map relationships between accounts based on shared attributes like IP addresses, device IDs, or payment information and identify clusters of interconnected accounts that may be controlled by the same individual. They can quickly adapt to emerging fraud schemes and give businesses timely alerts. These systems examine the user data and walk the gray line between normalcy and suspicious behavior, identifying inconsistencies.
3. Device Intelligence Platform
Tracking and analyzing IP addresses and device fingerprints (unique identifiers for a device based on its configuration, settings, and usage patterns) can flag accounts created from the same source, which is a common red flag for multi-accounting fraud.
4. Fraud Detection Platforms
Fraud detection platforms can help in real-time detection and prevention of fraud using machine learning, behavioral analytics plus large-scale data analysis. Such solutions monitor transactions for unusual behavior, respond to changing methods of fraudsters hence ensuring safety against loss.
By implementing robust fraud detection platforms, organizations can stay ahead of evolving threats and maintain compliance in the ever-changing financial landscape.
Final Thoughts
Staying ahead of fraudsters requires continuous technological advancements and vigilant updating of anti-fraud measures. As those seeking to game the system evolve their tactics, businesses must remain proactive in enhancing their defenses. Through ongoing vigilance, adaptability, and commitment to maintaining a fair and secure online environment, companies can strive for success in the digital era while effectively combating multi-accounting fraud. This constant improvement ensures that legitimate users are protected and fraudsters are consistently outmaneuvered.
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Frequently Asked Questions
How can you detect multi-accounting?
Detecting multi-accounting is possible through various ways that are layered up, these ranges from the usage of cookies, local storage, the use of geolocation and IP address information, device fingerprinting among others.
Which industries are affected by multi-accounting?
Multi-accounting can primarily target any industry which gives out bonuses for signing up or websites. However, gaming, ecommerce or any B2C brands offering online services or products can be impacted by multi-accounting fraud.
Is multi-accounting unlawful?
In technical terms, it is not illegal if you use your identity, but if you’re using another real person’s identity on this matter (which often happens) then it becomes unlawful.
