Cyberattacks are a growing menace—with a new hacking attempt launched every 39 seconds, 6.3 trillion intrusion attempts, and 5.5 billion malware attacks occurring in 2022 alone. In such a grave climate, organizations must be equipped to detect, prioritize, and respond to threats quickly. In fact, while the mean time to detect (MTTD) a breach was 207 days and the mean time to contain (MTTC) was 70 days in 2022, integrating a SIEM solution with user and entity behavior analytics (UBA) can dramatically bring those numbers down.

Given the sheer volume and sophistication of these threats, anomaly detection and fraud prevention have become well-known and essential approaches to identifying malicious activity in real-time. Here's how cutting-edge UBA transforms user behavior into a fraud-fighting superpower—and some real-world examples from our work at CrossClassify:

Anomaly Detection

• Advanced ML algorithms, statistical models, and heuristics enable UBA to catch subtle deviations while continuously updating a dynamic risk score.
• Key features include sudden location changes, typing speed variations, unusual page navigation patterns, and inconsistent time zone usage, with real-time alerts triggered when risk thresholds are breached.
• Fine-tuned thresholds ensure only truly risky behavior is flagged, helping reduce false positives.
• Our team at CrossClassify has leveraged these features to detect fraud in Account Takeover (ATO) and Account Opening (AO) domains:
  ATO fraud: When attackers use stolen credentials to access existing accounts.
  AO fraud: When fraudulent new accounts are created using fake or stolen identities.
  

These insights are critical not only for preventing ATO and AO but can also extend to blocking transactional data fraud.

Our Experience at CrossClassify
At CrossClassify, our team has successfully applied UBA to combat fraud in areas like account takeover and fraudulent account opening services. By analyzing behavioral cues such as location shifts, typing speeds, page navigation, and time zone variations, we’ve achieved actionable insights that significantly reduce fraud risks. These techniques also help prevent transactional data fraud, safeguarding your organization against a wide spectrum of financial crimes.

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