Behavioral biometrics: Next-Gen Fraud Prevention using AI & ML

Online payment fraud is believed to have cost e-commerce 41 billion US dollars worldwide in 2022, an increase from the previous year. By 2023, the amount is anticipated to increase even more, reaching 48 billion dollars.

According to’s analysis, merchants continue to spend one-tenth of their yearly e-commerce revenue on managing payment fraud globally.  In past 2 years, “Phishing / pharming / whaling” have been identified as no #1 fraud and around 43 % of total fraud.

Tell me about Behavioral Biometrics. What is it?

Customer verification using Password, PIN can be easily compromised. As a result, there is an increase in interest in using cutting-edge technology to address this growing problem. Behavioral biometrics can be of help to address frauds. With the help of this cutting-edge technology, authentication procedures go beyond knowledge-based security measures.

This technique is based on AI and ML. It is used to identify and retain patterns of user’s behavior.  Behavioral biometrics uses machine learning and artificial intelligence to recognize distinctive human behavioral characteristics.

What gets tracked?

Fraud may be detected using a variety of user activities, which are often tracked and examined using behavioral biometrics. The following typical user actions are used to spot probable fraud:

  • Typing Patterns and Speed: Every user has a different typing pace and style. Keystroke dynamics, such as the interval between keystrokes and the length of a key press, may be analyzed using behavioral biometrics to spot variations that can point to fraudulent login attempts.
  • Mouse Movements: Users’ mouse or trackpad movements are distinctive. To identify irregularities brought on by automated scripts or unauthorized users, fraud detection systems can examine mouse movements, including speed, acceleration, and route.
  • Navigation Patterns: A user’s pattern of behavior when visiting a website or application might be instructive. The user’s typical navigation habits are examined using behavioral biometrics, which searches for variations that could point to fraudulent activity.
  • Touch Gesture Analysis: On mobile devices, touch motions like tapping, swiping, or pinching can be examined to spot behavioral changes that could indicate fraudulent use.
  • Scrolling Behavior: The way people surf across a webpage or document can help distinguish between human users and artificial scripts or bots.
  • Time and Length of Sessions: Examining the length of a session, the time spent on various sites, and the timing of activity can assist identify abnormalities that can indicate questionable conduct.
  • Location and IP Address Analysis: Tracking the user’s geolocation information and contrasting it with their usual locations might assist in identifying unauthorized login attempts coming from strange places.
  • Device fingerprinting: By identifying a user’s device uniquely based on their screen resolution, operating system, and browser version, it is possible to identify unauthorized access attempts coming from unidentified devices.
  • Transaction History: Investigating a user’s transaction history might turn up odd spending habits or unforeseen transactions that can be a sign of fraud.
  • Language and Communication habits: Phishing attempts or illegal account access can be detected by examining the language used in communications and contrasting it with the user’s regular habits.

It’s important to note that the combination of these behavioral factors, rather than individual traits, is what makes Behavioral Biometrics effective in fraud detection.

Design Principle:

There are multiple industry principles and standards to develop robust, unbiased and more effective system. Here are few to consider. This is not an exhaustive list.

> Data Privacy: The use of biometric data for fraud detection must comply with data privacy regulations such as GDPR and CCPA.

> Bias and Discrimination: To develop unbiased and discriminative system, we need to make sure that the data being used is diverse and non-bias.

> Security: Biometric systems are vulnerable to various types of attacks, including spoofing, impersonation, and replay attacks. Therefore, it is crucial to implement robust security measures, such as liveness detection, anti-spoofing techniques, and secure storage of biometric data.

> Accuracy: Biometric systems must be accurate to prevent false positives and false negatives. Therefore, it is important to use high-quality sensors, optimize the algorithms, and regularly evaluate the performance of the system.

> Transparency: It is essential to provide transparent information about how the biometric system works, what data is collected, and how it will be used. This helps build trust between the users and the organization implementing the system.

> User Consent: Users must be fully informed and provide explicit consent before their biometric data is collected and used for fraud detection purposes. They should also have control over their data and be able to opt-out at any time.

> Accountability: Non-compliance with legal and ethical guidelines should be avoid by organizations and must be accountable in case they fail to do so.

How are AI & ML helping?

With the rapid development and accuracy being achieved in AI. Behavioral biometrics has benefited. AI and ML have contributed in various ways and improved result and user experience. Here is quick overview.

  • Data Collection & Processing: AI and ML algorithms are used to collect and process vast amounts of user behavioral data. This data includes typing patterns, mouse movements, navigation sequences, and other behavioral characteristics.
  • Pattern Recognition: AI and ML algorithms are trained to recognize patterns and establish a baseline of normal behavior for each user. It involves analyzing the behavioral data collected from legitimate users and identifying common patterns and trends for each user.
  • Anomaly Detection: Once the baseline of normal behavior is established, AI and ML algorithms continuously monitor user behavior in real-time. Any deviations or anomalies from the established patterns are flagged as potentially fraudulent activities.
  • Adaptive Learning: Behavioral Biometrics systems uses AI and ML algorithms to continuously update their understanding of normal user behavior. This allows the system to adapt to changes in user behavior over time and become more accurate in detecting fraud.
  • Real-Time Analysis: Behavioral Biometrics systems use AI and ML algorithms to analyze user behavior in real-time during an ongoing session. This allows for immediate detection of suspicious activities, enabling timely intervention to prevent fraud.
  • Risk Scoring: AI and ML algorithms assign risk scores to different user interactions based on the level of deviation from the established baseline. Higher risk scores indicate potential fraudulent behavior, while lower scores suggest normal user activity.
  • Multiple Data Source: AI and ML algorithms integrate data from multiple sources, such as typing behavior, mouse movements, and navigation patterns, to create a comprehensive user profile for more accurate fraud detection.
  • Reducing false positive: AI and ML algorithms work to minimize false positives by refining their understanding of normal user behavior. This ensures that legitimate users are not incorrectly flagged as potential fraudsters.

Conclusion: With ever increasing frauds, we will need to adopt newer techniques and be ahead of fraudsters. Behavioral biometrics is good technique to explore and adopt.


Author Details

Rahul Chakradhar Sale

Rahul is a Principal Solution Architect at Infosys Digital Experience. He architects microservices, Web Application/Mobile applications, and Enterprise cloud solutions. He helps deliver digital transformation programs for enterprises, by leveraging cloud services, designing cloud-native applications, and providing leadership, strategy, and technical consultation.

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