What Are Behavioral Biometrics and How Do They Prevent Fraud?

Behavioral biometrics analyze a user's interaction pattern with their device, keyboard, mouse and screen as a mechanism to verify the identity of the user and thwart fraud attempts. The emphasis is placed on behavioral patterns as opposed to traditional biometrics such as facial recognition or passwords. 

If you are a risk manager at a bank, a fintech company or payment processor, you are likely familiar with the limitations of one-time or multi-factor authentication protocols and have seen how easily fraudulent actors can take over user accounts with traditional security measures. Behavioral analytics offer an elegant solution to the problem by working in the background and continuously assessing the behaviour of the user without disrupting the digital experience.

How Do Behavioral Biometrics Work?

When a user logs in to their account or opens the application, behavioral biometric engines record hundreds of parameters. Even if a fraudster can steal or guess a user’s credentials, the behavioral patterns recorded by the engine would be completely different from those of the account owner. 

The most common attributes captured by behavioral biometric engines are:

Keystroke dynamics – time to press a key, pause time between keystrokes etc.

Device controls – swipe dynamics, scroll speed and acceleration, phone orientation

Navigation – time spent on pages and journey between pages

Digital footprints – IP addresses, browser fingerprints and VPN detection

Traditional Biometrics vs. Drona Pay Behavioral Biometrics

Feature	Traditional Biometrics (Fingerprint/Face ID)	Drona Pay Behavioral Biometrics
Authentication Point	Single point (usually at login)	Continuous (monitored throughout the entire session)
User Friction	Requires deliberate user action	Completely invisible to the user
Spoofing Risk	Can be bypassed if the physical device is compromised	Analyzes unmimickable human and bot behavior patterns
Primary Use Case	Initial identity verification	Continuous risk scoring and real-time ATO prevention
Processing Scale	Device-level execution	High-velocity, real-time decisioning at 5,000 TPS

Why Are Financial Institutions Embracing Behavioral Analytics?

Modern fraudsters use increasingly sophisticated bots and Remote Access Trojans (RATs) to take over user accounts. Traditional security tools can detect only a limited number of fraud patterns. By contrast, analyzing both human and machine behavior allows financial institutions to take on the most pressing fraud risk challenges of today, including:

Preventing account takeover (ATO)

Instantly detect anomalous activity such as in-app navigation or keystroke patterns that differ from the expected behavioral patterns of the user in real time.

Fraud prevention for scams and social engineering attacks

Detect hesitation or irregular mouse movements during high-risk operations such as fund transfers or identify when the user has placed the phone down while a call is active as an indicator of potential social engineering.

Detect bot farms

By analyzing behavioral biomarkers, it is possible to detect anomalies such as non-human mouse movements, linear movements, and other patterns before bots initiate fraudulent activity or create fake accounts.

Securing Payments With Drona Pay

As a leading player in the fraud and risk space, we have developed our platform with one goal in mind – enable customers to offer best-in-class digital experiences with minimal friction. 

Our fraud prevention solution works in the background and leverages behavioral biometrics to protect users, apps, and businesses from a wide range of fraud types, including account takeover, social engineering, and bot traffic. By analyzing keystroke patterns, IP footprints, and device fingerprints we can score transactions in real time and detect anomalous patterns that may indicate fraud.