Why Behavioral Analytics is Key to Fraud Detection Today

Data breaches and cyber attacks increase every year, putting significant stress—and costs—on firms worldwide. According to Statista, the average cost of a data breach alone is around $4.35 million.

According to Sumsub’s internal statistics, over 70% of fraud happens beyond the onboarding stage. Therefore, it is essential to protect the whole user journey—and this is where behavioral analytics can help.

Let’s explore what behavioral analytics is and why it’s one of the most important strategies for business to detect and prevent financial crime.

What is behavioral analytics?

Behavioral analytics examines patterns of behavior. When it comes to businesses, the way that customers interact creates a pattern—and ultimately a profile—of expected behavior. This can include the times of day people usually log into the app, the types of transactions they usually perform, devices they usually use, and even the ways they use their keyboard.

Behavioral analytics often involves automation and machine learning, tracing behavioral patterns that humans would miss and spotting anomalies both on an individual level and across an organization.

Behavioral analytics and fraud detection

Behavioral analytics is widely used to identify and flag possible incidents of fraud, unusual transactions, or other criminal activity by examining patterns of behavior. Here’s how:

• Establishing baseline behavior. Behavioral monitoring helps establish a baseline of typical behavior for each user. By analyzing historical data and patterns, the system learns what’s considered usual behavior for an individual user. 
• Real-time monitoring. Fraud monitoring analytics usually run in real-time, enabling immediate detection of suspicious activity. For example, if a user suddenly demonstrates behavior that is inconsistent with their normal patterns, such as accessing multiple accounts within a short period, demonstrating atypical purchasing patterns, or making unusually large transactions, the system can trigger an alert for potential fraud.
• Machine learning algorithms. Behavioral monitoring systems often incorporate machine learning algorithms to continuously improve their fraud detection analytics. These algorithms can adapt to new fraud patterns and identify previously unknown suspicious behaviors based on large volumes of data. By leveraging machine learning, such systems become more accurate in detecting fraud over time.
• User profiling and risk scoring. Behavioral monitoring systems create user profiles based on behavior patterns. Each user can be assigned a risk score that indicates whether they may be involved in fraud activity. Risk scores are typically calculated based on historical data, detected anomalies, and machine learning algorithms. Higher-risk profiles can be subjected to additional scrutiny or authentication measures.
• Early detection and prevention of fraud. By monitoring users in real-time, behavioral monitoring tools can detect potential fraud at an early stage, before a financial loss occurs. Early detection enables prompt action to prevent fraudulent transactions, such as blocking suspicious activities, notifying users, or escalation of cases to law enforcement.
• Adaptive security measures. Behavioral monitoring can help implement adaptive security measures. For example, if a user demonstrates high-risk behavior, additional authentication steps, such as a Liveness check, may be required. This adds an extra layer of security to protect against crime.

What is behavioral biometrics?

Behavioral analytics and behavioral biometrics are related concepts, but have distinct differences.

Unlike behavioral analytics, behavioral biometrics refers to the use of unique behavioral characteristics or traits of an individual for identification or authentication purposes. It involves capturing and analyzing specific patterns of human behavior, such as keystroke dynamics, mouse movement, touchscreen gestures, voice patterns, or even the way individuals walk. Behavioral biometric traits are used to create a unique user profile, and any future interactions or transactions can be compared against this profile for verification purposes.

While behavioral analytics focuses on detecting fraud by analyzing various behavioral indicators and identifying anomalies, behavioral biometrics uses specific behavioral traits as biometric identifiers for user identification or authentication purposes.

According to SNS Insider, the behavioral biometrics market alone was valued at USD 2.01 billion in 2022, and is projected to reach USD 11.64 billion by 2030.

FAQ

• What is behavioral analytics?

  Behavioral analytics is a scientific discipline that examines patterns of behavior.

• What is behavioral biometrics?

  Behavioral biometrics refers to the use of unique behavioral characteristics or traits of an individual for identification or authentication purposes.

• How is analytics used in fraud detection?

  Behavioral analysis uses patterns of behavior to identify and flag possible incidents of fraud, unusual transactions, or other criminal activity.