Velocity abuse is an increasingly critical issue in the fintech industry, with severe implications for businesses and users alike. In simple terms, velocity abuse refers to an attacker rapidly executing malicious activities – such as account creation, login attempts, or transaction requests – in an effort to overwhelm security measures, exploit vulnerabilities, or fraudulently gain access to accounts and sensitive data.

For fintech firms operating within the Fiserv ecosystem, velocity abuse can lead to substantial financial losses, damage to brand reputation, and a diminished user experience for legitimate customers. Furthermore, an inability to effectively combat velocity abuse can result in non-compliance with regulatory requirements, leading to potential penalties and legal sanctions.

The article's target audience – fintech professionals, cybersecurity specialists, enthusiasts, regulators, and potential Verisoul users – must be well-versed in the latest techniques and strategies to counteract velocity abuse, particularly given its growing prevalence and sophistication. Five key strategies will be explored as part of the article, each with its relevant tactical implementations. They are:

1. Device and Browser Fingerprinting
2. Bot Behavior Biometrics AI
3. Emulator and Virtual Machine Detection
4. Impossible Travel
5. Advanced Captcha

These approaches can be employed individually or in combination, ultimately providing a robust and flexible defense against malicious actors and their increasingly diverse tactics. By implementing a multi-pronged strategy and continuously refining existing measures, fintech professionals and cybersecurity experts can better safeguard their platforms from the damaging effects of velocity abuse.

Stay ahead of the threats posed to the fintech landscape by familiarizing yourself with these strategies and being proactive in implementing effective measures. By adequately addressing the challenges of velocity abuse, you can minimize risks, ensure compliance, and maintain a secure environment for your business, users, and partners.

Strategy 1: Device and Browser Fingerprinting

What is Device and Browser Fingerprinting

Device and browser fingerprinting involve collecting unique device and browser identifiers to track users. This method is widely used by businesses to detect and prevent fraudulent activities, such as velocity abuse and account takeovers.

How does it work

Fingerprinting combines various device configurations, settings, and browser data to create a unique identifier for each user. Some examples of data points used for fingerprinting include IP address, user agent, screen resolution, and installed fonts. By examining these characteristics, businesses can differentiate between legitimate and malicious users.

Pros & Cons

Pros:

• Highly accurate identification: Device and browser fingerprinting provides a precise and reliable way to identify users based on their devices' unique attributes, making it difficult for attackers to spoof their identity.
• Ability to track devices across multiple IP addresses: As fingerprinting collects various device-related data, it can keep track of users even when their IP addresses change, thereby enhancing fraud detection capabilities.

Cons:

• Potential privacy concerns: Collecting a substantial amount of user data for fingerprinting could raise privacy concerns, making it imperative for businesses to comply with data protection regulations and be transparent with their users.
• Sophisticated attackers may circumvent fingerprinting: Tech-savvy adversaries may leverage techniques such as browser extensions, VPNs, or device spoofing to bypass fingerprinting checks, requiring constant vigilance and updates to stay ahead.

Tactical Implementation

1. Using device/browser fingerprinting libraries: Numerous libraries and tools (e.g., FingerprintJS) are available to streamline the implementation of fingerprinting within your platform, reducing the need for custom development.

   
   

2. Customizing checks for known fingerprint evasion techniques: Debug and devise measures for detecting common tactics used by attackers to bypass fingerprinting (e.g., browser spoofing, switching user-agent strings, using VPNs, etc.).

   
   

3. Monitoring changes in device fingerprints to detect account takeovers: Establish a process to closely monitor alterations in device fingerprints, such as a new device suddenly accessing a user's account. This can be a vital early warning system to detect and prevent account takeover attempts.

   
   

By appropriately implementing device and browser fingerprinting within your fintech platform, you can enhance your ability to detect and prevent velocity abuse, ultimately safeguarding your business and end users. However, always keep in mind that this strategy should be part of a more comprehensive approach, utilizing multiple tactics to ensure the highest level of security against ever-evolving threats in the fintech landscape.

Strategy 2: Bot Behavior Biometrics AI

What is Bot Behavior Biometrics AI

Bot Behavior Biometrics AI is a technology that analyzes user behavior patterns to differentiate between genuine users and bots. By leveraging artificial intelligence (AI) and machine learning algorithms, this approach helps fintech professionals and cybersecurity experts monitor and detect suspicious activity that may be indicative of velocity abuse.

How does it work

Bot Behavior Biometrics AI uses machine learning algorithms to assess patterns in user interactions, such as mouse movements, keystrokes, and the timing of interactions. By analyzing these patterns, the AI can determine whether the actions taken on a platform are more consistent with human behavior or automated bot behavior. This information can be used to block or restrict access to fraudulent accounts, helping protect fintech organizations from velocity abuse and other cybersecurity threats.

Pros & Cons

Pros:

• Accurate bot detection: AI-based bot behavior biometrics systems can accurately identify automated bots, reducing the risks associated with fraudulent transactions and account activities.
• Adaptive: As new bot tactics emerge, machine learning models can be continuously updated, ensuring that the system remains effective against evolving threats.

Cons:

• Compute-intensive: AI-based bot detection may require significant computational resources, which could be a downside for some fintech organizations with limited resources.
• False positives: Although AI systems are generally accurate when detecting bots, false positives can occur. These instances may lead to legitimate users being mistakenly flagged as bots, which could negatively impact the user experience.

Tactical implementation

To implement a Bot Behavior Biometrics AI solution effectively, follow these key steps:

1. Integrate AI-based bot detection tools: Choose a comprehensive bot detection tool, such as DataDome or BotGuard, that utilizes AI and machine learning algorithms to analyze user behavior patterns.

   
   

2. Continuously update machine learning models: Regularly update your models to adapt to new bot tactics and ensure that your system remains effective against emerging threats. This may involve retraining your models with new data or tuning your algorithms to improve accuracy.

   
   

3. Customize user interaction monitoring: Tailor your monitoring of user interactions to reflect the specific patterns and behaviors associated with your platform. This customization can help reduce the likelihood of false positives and improve overall detection accuracy.

   
   

By implementing these tactics, fintech professionals and cybersecurity experts can better defend their organizations against velocity abuse and the damaging effects of automated bots on their systems.

Strategy 3: Emulator and Virtual Machine Detection

What is Emulator and Virtual Machine Detection

Emulator and Virtual Machine Detection refers to the process of identifying users attempting to access fintech platforms from emulators or virtual machines (VMs). Emulators are software programs that replicate the functioning of a specific hardware environment, while VMs are software programs that imitate the operating system of a particular computer. Attackers often use these systems to conduct fraudulent activities or bypass security measures in place.

How does it work

The detection technique works by analyzing hardware, software, and runtime environment characteristics to differentiate between native systems and those running on emulators or VMs. For instance, virtual machines often have a unique set of hardware configurations and software fingerprints that can be used to identify them.

Pros & cons

• Pros: Emulator and VM detection can be an effective measure against distributed attacks and helps in identifying fraudulent accounts. It aids in exposing attackers who attempt to clone devices or fake device information to evade security systems.
• Cons: This technique may generate false positives for legitimate users who rely on VMs for work, testing, or other purposes. Also, sophisticated attackers may use advanced evasion techniques to bypass detection systems.

Tactical implementation

To implement emulator and virtual machine detection effectively within a fintech environment, consider the following steps:

• Leverage existing libraries or tools: Utilize pre-built tools like VMRay or VMDE to detect emulators or VMs. These tools can assist in identifying known emulator or VM signatures and characteristics.
• Implement ongoing monitoring: Continuously monitor device characteristics and user behavior to detect deviations from established patterns. Flag any suspicious activity for further investigation.
• Introduce security measures: If an emulator or VM is detected, fintech platforms might choose to alert the user, temporarily restrict access, or enforce additional security checks to ensure the user's authenticity.

By employing these tactics, fintech professionals and cybersecurity experts can add another layer of defense in combating velocity abuse and protecting their platforms from fraud.

Strategy 4: Impossible Travel

What is Impossible Travel

Impossible travel refers to the detection of simultaneous logins or access from distant locations within a short timeframe, indicating potential fraudulent activity or account takeover attempts. By monitoring geolocation data and user access patterns, fintech organizations can identify improbable or physically impossible user movements that may signify the presence of a threat actor in their systems.

How does it work

The impossible travel prevention method involves continuously monitoring user access patterns and comparing the geolocation data associated with each login or transaction. When multiple logins or transactions are detected originating from distant locations within a short time frame, it is flagged as improbable or impossible travel, which likely indicates fraudulent activity, IP spoofing, or VPN usage to mask the attacker's real location.

Pros & cons

Pros:

1. Quick detection of IP spoofing, VPN usage, and other fraudulent activities: The impossible travel detection method allows for the fast identification of potential threat actors attempting to access accounts or carry out transactions using spoofed IP addresses or VPNs.
2. Low false-negative rate: Since improbable user travel patterns are uncommon, false negatives are relatively low with this strategy.

Cons:

1. False positives for legitimate users on VPNs or traveling rapidly: Some legitimate users may be flagged for impossible travel due to using VPNs or legitimately accessing their accounts from distant locations within a short timeframe (such as frequent travelers, remote workers, or those connecting through multiple devices).
2. May require additional verification measures: In some cases, flagged users may need to undergo additional verification steps to confirm their authenticity, potentially causing friction or delays in their online experience.

Tactical implementation

To effectively implement impossible travel detection in your fintech organization, consider the following steps:

1. Use geolocation APIs to retrieve and compare user locations: Integrate geolocation APIs (such as MaxMind GeoIP2 or IPstack) into your platform to gather and compare accurate user location data based on their IP addresses. This will enable you to detect unusual access patterns and flag suspicious activity.

   
   

2. Set custom travel thresholds to flag suspicious activity: Establish pre-defined travel thresholds (such as distance, time, and number of access attempts) to identify high-risk or impossible travel patterns. For example, you may flag users who log in from locations more than 1,000 miles apart within a 1-hour timeframe as suspicious.

   
   

3. Implement alerts or temporary restrictions based on impossible travel detection: Configure your system to send real-time alerts to your cybersecurity team when impossible travel is detected, allowing them to take immediate action. Additionally, consider employing temporary account restrictions or additional verification steps for users flagged for impossible travel to prevent unauthorized access and potential fraud.

   
   

Strategy 5: Advanced Captcha

What is Advanced Captcha

Advanced Captcha refers to complex response tests, such as image recognition, that are designed to distinguish humans from bots. These tests have evolved from traditional text-based Captcha tests to include more sophisticated challenges that are more difficult for automated tools to solve.

How does it work

Advanced Captcha works by requiring users to complete tasks involving image recognition, puzzle solving, or other non-text-based challenges to prove they are human. For example, a user may be asked to identify all images containing a specific object, such as a car or a tree. These challenges are designed to be easily solvable by humans but difficult for bots and automated tools.

Pros & cons

• Pros:
  • Highly effective against automated tools: Advanced Captcha tests are specifically designed to be challenging for bots, making them a strong deterrent against automated threats.
  • Multiple layers of security: Implementing an advanced Captcha system can add an additional layer of protection to existing cybersecurity measures, further safeguarding against velocity abuse.
  
  
• Cons:
  • Potential user friction: Some users may find Captcha tests frustrating or time-consuming, potentially leading to user dissatisfaction and higher bounce rates.
  • Decreased accessibility for some users: Advanced Captcha tests can be difficult or impossible for users with certain disabilities or impairments to complete, limiting access to the platform for these individuals.
  
  

Tactical implementation

To implement advanced Captcha on your platform, consider the following steps:

1. Leverage modern captcha services: Services such as reCAPTCHA and hCaptcha offer easy integration with fintech platforms and provide a variety of captcha challenges.

   
   

2. Customize challenge types and difficulty based on platform usage patterns: Analyze user behavior on your platform to determine the most appropriate captcha tests to implement. This customization can help reduce user friction while maintaining robust security measures.

   
   

3. Continuously monitor captcha efficacy: Advanced Captcha is only as effective as its ability to stay ahead of evolving bot technology. Regularly review the success rate of your Captcha system and be prepared to update as needed to maintain a high level of protection against velocity abuse.

   
   

Final Thoughts and Next Steps

Addressing velocity abuse in the fintech industry is crucial for protecting both businesses and customers from fraud and financial losses. Throughout this article, we discussed five key strategies that fintech professionals and cybersecurity experts can implement to combat this emerging threat:

• Device and browser fingerprinting for accurate user identification
• Bot behavior biometrics AI to detect and differentiate between genuine users and bots
• Emulator and virtual machine detection to identify non-native systems and potential attackers
• Impossible travel detection for quick identification of IP spoofing and other fraudulent techniques
• Advanced Captcha to create an additional layer of security against automated tools

However, it's important to not solely rely on these strategies alone. A holistic approach to cybersecurity should be continuously re-evaluated and updated to stay ahead of the evolving threats in the fintech landscape.

In addition to implementing these strategies, fintech professionals should:

• Invest in education and training for their teams to ensure everyone understands the latest risks and techniques related to velocity abuse
• Stay informed about new and emerging cybersecurity technologies by attending conferences, webinars, and networking events
• Collaborate and share information with other fintech and cybersecurity professionals to stay up-to-date on the latest threats and best practices

By taking these steps and embracing a culture of continuous learning, fintech organizations can build a strong, proactive defense against velocity abuse and other emerging cybersecurity threats.