BehavioSec

Fraud is a rapidly growing cybersecurity threat and a major economic drain, with cybercrime damages projected to reach $10.5 trillion by 2025. Many successful cyberattacks and online fraud schemes stem from compromised digital identities, often enabled by weak identity security and insufficient authentication assurance. Account TakeOver (ATO) occurs when stolen credentials are used to access accounts without authorization, frequently via bot-driven credential stuffing that exploits breached username/password dumps. Because users manage too many passwords to avoid reuse effectively, stronger controls such as Multi-Factor Authentication (MFA) and risk-adaptive authentication are central to reducing ATO. Targets span nearly all consumer account types, while in enterprise settings privileged/admin accounts are especially valuable because they enable data exfiltration, ransomware delivery, and exposure of PII, creating major regulatory and operational consequences.

Regulatory and industry initiatives aim to raise authentication assurance in payments. PSD2 mandates Strong Customer Authentication (SCA) based on two of three factors (knowledge, possession, inherence) and requires ongoing risk analysis to minimize high-friction authentication prompts. EMVCo’s 3-D Secure 2.x (3DS2) similarly strengthens payment authentication using extensive device data and behavioral biometrics.

New Account Fraud (NAF), including synthetic identity fraud, abuses stolen or assembled PII to create accounts for illicit activity such as laundering funds, sometimes leveraging recruited “money mules.” Identity assurance can be improved through in-person or remote ID verification plus physical and behavioral biometrics. Behavioral biometrics profile how users interact with devices—keystrokes, swipes, pressure, sensors, and location/network signals—enabling baseline comparisons that support low-friction approvals, bot detection, and portability across device upgrades.

BehavioSec’s platform (BehavioSense plus JavaScript/SDK collection) applies ML-driven risk scoring to behavioral biometrics and device intelligence to reduce ATO and NAF, support PSD2/3DS2-aligned assurance, and integrate with enterprise authentication ecosystems via standards and IAM integrations, offered through flexible deployment and licensing models.

• Cybercrime costs are estimated to reach $10.5 trillion by 2025.
• Account TakeOver fraud is frequently executed by malicious bots using credential stuffing with breached username/password dumps from dark web forums.
• Privileged enterprise accounts enable attackers to exfiltrate trade secrets, intellectual property, customer records, and PII more easily.
• Hijacked user accounts have been used to deliver ransomware payloads in many ransomware cases.
• Fraudsters achieve a 33% success rate even within the finance industry, extrapolating from a UK Finance 2021 report.
• PSD2 defines Strong Customer Authentication as using two of three factors: something you know, something you have, or something you are.
• EMVCo developed the 3-D Secure 2.x specification to improve payment security with strong authentication at transaction time.
• Synthetic fraud commonly uses PII such as names, emails, addresses, phone numbers, driver’s license numbers, and social security numbers.
• BehavioSec is ISO 27001 certified and SOC 2 Type 2 certified.
• BehavioSec offers licensing either per-user per period (month/year) or per transaction.

• “Fraud has emerged as one of the biggest and still growing threats in cybersecurity.”
• “The vast majority of successful cyberattacks, as well as various forms of online fraud, can be attributed to compromised digital identities.”
• “Applying that conventional wisdom is insufficient due to the sheer number of passwords that users have to maintain.”
• “Successful takeovers of enterprise accounts therefore lead to theft of key information, and open the door to massive fines for loss of PII, and lost productivity and damages caused by ransomware detonation.”
• “PSD2 also requires ongoing analysis of risk factors to mitigate the need for explicit SCA events, which add friction to the consumer experience.”
• “Users physically interact with their devices in ways that can be quantified to build consistent profiles.”
• “Since bots activities are scripted, their behavior usually looks different from real human website interactions.”
• “JavaScript is commonly used by FRIP solutions for behavioral biometrics and device intelligence, and fraudsters have learned to reverse engineer JavaScript.”
• “The use of Machine Learning (ML) based detection models is both a necessity and the current state of the art in the fraud reduction technology space.”
• “Risk-adaptive authentication systems that incorporate the unobtrusive examination of device and behavioral biometrics information can decrease the need for explicit authentication events which require user interaction.”