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 businesses to detect and prevent financial crime.

What is behavioral analytics?

Behavioral analytics examines patterns of behavior. For businesses, this means analyzing how customers interact, discerning a pattern—and ultimately a profile—of expected behavior. This can include the times of day that people usually log into an app, the types of transactions they usually perform, the devices they usually use, and even the ways they use their keyboard.

Behavioral analytics often involves automation and machine learning, enabling businesses to trace behavioral patterns that humans would usually miss.

Behavioral fraud detection

Below are some behavioral red flags that can allow companies to detect fraud in a timely manner:

• Atypical size of transactions — when a customer unexpectedly makes large purchases 
• Unusual transaction patterns — when a customer purchases identical items or makes several purchases for the same amount of money
• Location changes — when a customer logs in from a new country or region. This one is especially suspicious when a customer logs in from a high-risk country
• Suspicious login attempts — when a customer changes their password several times or fails to log in
• Unusual interaction patterns — when a customer shows atypical typing patterns or touch gestures
• Changes in user information — when a customer provides a new shipping address, phone number, payment method, etc.

The future of behavioral fraud detection

The behavioral analytics will keep evolving as AI advances, fraudsters will similarly improve their methods. Therefore, a strong fraud detection solution will be required to meet the following challenges:

• Rapidly developing fraud schemes 
• The need to balance data privacy and data sufficiency
• The need to interpret collected data and build rules based on it without interfering with legitimate users 

The main challenge that behavioral analytics possesses is the need to balance data collection with privacy Therefore, it’s essential for companies to be transparent when it comes to data collection, gathering only the minimal amount of data required alongside strong encryption.

Fortunately, coming advancements in technology will require less data to be collected for sufficient fraud prevention.

Solutions

Behavioral analytics is widely used to identify and flag possible incidents of fraud, including unusual transactions and other criminal activity. 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 unusually large transactions, the system can trigger an alert for potential fraud and request additional user verification.
• 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 big data. By leveraging machine learning, these 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.

All of these tools are part of Sumsub’s Behavioral Biometric Fraud Prevention. Our solution analyzes multiple events and data from user devices throughout the entire lifecycle to create dynamic profiles that fraudsters cannot replicate. 

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 physical traits of an individual for identification or authentication purposes. 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 physical traits (e.g., fingerprints, face ID) as biometric identifiers for user 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.