How to Prevent Account Sharing with Behavioral Biometrics

Streaming services, online learning platforms, and digital subscriptions have revolutionized how users access content. However, account sharing abuse has become a growing challenge, allowing multiple people to use a single paid account—often in violation of a service’s terms.

While some companies tolerate limited sharing within households, unauthorized access can result in substantial revenue losses and fraudulent activity, ultimately undermining business models that rely on individual user subscriptions. Platforms like Netflix, Spotify, and DAZN have begun implementing stricter measures to curb excessive account sharing, recognizing that conventional login security methods—such as passwords and multi-factor authentication—often fall short in detecting whether a legitimate account owner is actually the one logging in.

Instead of relying on intrusive security measures that frustrate genuine users, businesses are shifting towards frictionless detection methods like behavioral biometrics—which analyze user behavior in real-time to differentiate between legitimate users and unauthorized account sharers. This article explores how behavioral biometrics can help mitigate account sharing abuse, providing a seamless yet highly effective approach to login security.

What is account sharing?

Account sharing refers to multiple individuals accessing a single account, often beyond what a service provider allows. While some users engage in casual sharing within families or among friends, others exploit it to circumvent paid subscriptions entirely, leading to widespread revenue losses.

How does account sharing abuse happen?

There are various ways account sharing abuse can occur. Let’s take a look at a few examples below: 

Intentional sharing
Many users knowingly share login credentials with family, friends, or even co-workers—despite terms of service often restricting such behavior. This is particularly common with streaming platforms, gaming services, and educational tools that allow multiple logins.

Unintentional sharing
Some users unknowingly leave their credentials stored on shared devices, workplace computers, or public networks, allowing unintended access. Over time, login credentials may spread to multiple users, even without the original account holder’s knowledge.

Credential theft & reselling
Cybercriminals steal login details through phishing attacks or data breaches and sell them on the dark web. Subscription-based services are often targeted, with entire marketplaces dedicated to selling stolen or shared premium accounts at a fraction of the cost.

Bots & automation
Some fraudsters use automated bots to test login credentials against multiple accounts, exploiting weak passwords or leaked login data. These scripts bypass basic security checks, allowing unauthorized users to continuously hijack legitimate accounts without being detected.

With businesses losing billions to unauthorized account usage, it’s no longer just an ethical concern—it’s a serious financial risk that demands intelligent fraud prevention solutions that do not inconvenience legitimate customers.

Impact of account sharing on major streaming services

The widespread practice of account sharing has had a major financial impact on digital subscription businesses. While some companies initially tolerated limited sharing, the growing scale of abuse has forced them to take action. The numbers reveal the true cost of unauthorized access:

Netflix’s response to account sharing
For years, Netflix allowed multiple users within a household to share an account. However, as the practice expanded beyond families, the company estimated that over 100 million households worldwide were sharing passwords. This resulted in substantial revenue losses, prompting Netflix to enforce stricter sharing policies. When the company rolled out its password-sharing crackdown in 2023, the results were immediate and significant. In late May 2023, Netflix saw an unprecedented surge in new subscribers, with nearly 100,000 daily sign-ups on May 26 and 27, marking its biggest user acquisition spike in over four years. This shift demonstrated that many users had been relying on shared accounts rather than paying for their own subscriptions.

Spotify and the cost of shared passwords
Spotify, one of the largest music streaming platforms, has also faced account-sharing abuse. A 2021 survey revealed that 22.2% of Spotify subscribers were either giving or receiving access to a paid account without actually paying for a subscription themselves. While Spotify has introduced family plans and student discounts, unauthorized access continues to impact subscription revenue and fair artist compensation.

Industry-wide revenue losses
The financial impact of account sharing extends beyond individual companies. A study by Parks Associates estimated that in 2021 alone, password sharing cost streaming services approximately $9.9 billion in lost revenue. As digital subscriptions grow across industries—ranging from video and music streaming to software and gaming platforms—the economic consequences of unauthorized access are becoming harder to ignore.

With more companies now recognizing the need for secure yet seamless account authentication, the challenge lies in identifying unauthorized users without alienating legitimate customers. Behavioral biometrics presents an effective way to tackle this issue without disrupting the user experience.

Dealing with the challenges of account sharing

For many businesses, account sharing presents a double-edged sword—cracking down too hard can alienate legitimate users, while ignoring it results in significant revenue losses and security vulnerabilities. Streaming services, digital subscriptions, and online platforms face three major challenges when addressing this issue:

1. Balancing security with user experience
Users expect seamless access to their accounts, whether they’re logging in from a new device, traveling abroad, or switching between platforms. Excessive login restrictions—such as mandatory two-factor authentication (2FA) for every session—can drive frustration and push customers toward competitors.

2. Identifying unauthorized users without blocking legitimate ones
A user logging in from a different device does not automatically mean account sharing abuse is happening. Some services implement IP-based restrictions or limit logins per device, but these methods often fail to distinguish between real users and bad actors. For example, a traveling user may log in from a new location, triggering an unnecessary security block. Also, a household sharing a Wi-Fi connection might appear as a single user, making it harder to detect real abuse.

3. Stopping fraudulent account sharing without increasing friction
Traditional anti-fraud measures like frequent password resets, SMS-based verification, or limiting the number of concurrent streams often lead to higher support requests and customer churn.

Instead, businesses need smarter, passive security solutions that:

• Detect suspicious activity without disrupting genuine users.
• Analyze behavioral patterns rather than relying on static credentials.
• Provide real-time insights into whether an account is being misused.

This is where behavioral biometrics comes into play—a frictionless, real-time authentication method that helps businesses distinguish between legitimate users and unauthorized account sharers without interrupting the user experience.

Behavioral biometrics: a smarter way to detect account sharing

Traditional security methods like passwords and two-factor authentication (2FA) focus on verifying what a user knows (passwords, PINs) or what they have (SMS codes, authentication apps). However, they fail to verify who is actually behind the screen. This is where behavioral biometrics offers a more effective approach. Behavioral biometrics analyzes how a user interacts with a platform, identifying unique behavioral patterns that differentiate legitimate users from unauthorized ones. Unlike passwords, which can be stolen or shared, behavioral biometrics is passive, continuous, and highly accurate in detecting anomalies that indicate account sharing.

How behavioral biometrics helps prevent account sharing

Real-time behavioral data reveals patterns that conventional security measures often overlook, making it easier to detect irregularities. Some key behavioral biometrics techniques include:

Keystroke dynamics

• Every user has a unique typing rhythm—the speed, pressure, and timing of keystrokes can indicate whether the account owner is actually logging in.
• If an account suddenly exhibits a different typing pattern, it could suggest that a different person is using the credentials.

Mouse activity & cursor movements

• Mouse movement patterns, scrolling speed, and cursor positioning tend to be consistent for each user.
• If an account shows erratic, unnatural, or inconsistent mouse behavior, it may indicate a bot or an unauthorized user.

Session duration & interaction timing

• Platforms can track how long users spend on certain pages and how quickly they navigate through different sections.
• Sudden short or excessively long interactions may indicate fraudulent activity or automation scripts trying to bypass detection.

IP & Device Intelligence

• Unexpected device switches and logins from unusual locations are strong indicators of account sharing.
• Platforms can analyze historical login patterns to detect anomalies, flagging logins that don’t match a user’s usual behavior.

Unlike rigid security measures that rely on blocking access based on login location or device, behavioral biometrics provides a dynamic way to detect anomalies without disrupting legitimate users. These methods allow businesses to recognize unauthorized account usage more accurately while keeping the login experience seamless for genuine users.

Trustfull’s login authorization: ensuring secure logins without disrupting users

Detecting account sharing requires a balance between security and user convenience. Restrictive measures, such as frequent password resets or strict device limitations, often frustrate legitimate users. Instead, a more effective approach relies on analyzing user behavior and device characteristics in the background, ensuring security without disrupting access.

Frictionless authentication without interruptions
Traditional login security often forces users to verify their identity through additional steps, increasing friction. This particular approach operates silently in the background, using behavioral biometrics and device fingerprinting to verify whether the correct person is accessing the account.

Spotting unauthorized access attempts
Each login event provides multiple data points that help distinguish between genuine users and unauthorized account sharers. A combination of typing patterns, cursor movements, and device settings makes it possible to detect when someone new is using an account, even if the credentials are correct.

Evolving security through self-learning authentication
Rather than relying on static rules, Trustfull’s system adapts to user behavior over time, improving accuracy in detecting anomalies. As more data is collected, authentication models become better at identifying inconsistencies, making it harder for account sharers to go unnoticed.

Detect spoofing attempts
Fraudsters often try to mask their real devices using VPNs, private browsers, or virtual machines. Trustfull’s device intelligence system analyzes key browser and system characteristics to identify attempts at device spoofing. Unusual device configurations or repeated logins from different hardware can indicate unauthorized access.

IP Intelligence to flag suspicious logins
IP data plays a crucial role in identifying login patterns that don’t match the account owner’s typical behavior. Anomalies, such as frequent logins from different geographic locations or repeated access attempts from flagged IP addresses, signal potential account sharing or credential misuse.

Comprehensive login activity monitoring
Instead of reacting to individual login events, Trustfull’s system tracks authentication trends over time, offering deeper insights into potential security threats. Businesses gain access to detailed reports, helping them understand when and where account sharing occurs and enabling them to take action as needed.

The key to mitigating account sharing

Account sharing remains a complex challenge for digital businesses, particularly for subscription-based services, online learning platforms, and financial applications. While some level of sharing is expected, excessive abuse leads to revenue loss, security risks, and account integrity issues. Addressing this problem requires more than password restrictions or rigid device limitations—it demands an intelligent, user-friendly approach.

Behavioral biometrics offers a powerful way to distinguish between legitimate users and unauthorized access attempts. Unlike traditional authentication methods, which rely solely on passwords and device checks, analyzing user behavior provides deeper insight into login authenticity. 

Businesses looking to reduce account sharing without disrupting user experience can benefit from security solutions that operate silently in the background, verifying users based on their natural typing patterns, mouse activity, device attributes, and login history. These methods help ensure genuine users retain seamless access while unauthorized login attempts are flagged and addressed proactively.

Let’s find out how your organization can overcome the challenge of account sharing.

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Get in touch with our fraud prevention team.