Detect and prevent insider threats with real-time data processing and machine learning

Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent. PwC’s 2018 Global Economic Crime and Fraud Survey reveals that people inside the organization commit 52% of all frauds. Information security breaches originating within a bank can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations.
Ignoring any internal security breach poses as much risk as an external threat such as hacking, especially in a highly regulated industry like banking. Some of the dangers of insider threats in the banking and financial industry include:

  • Exposing the PII information of the customers
  • Jeopardized customer relationship
  • Fraud
  • Loss of intellectual property
  • Disruption to critical infrastructure
  • Monetary loss
  • Regulatory failure
  • De-stabilized cyber assets of financial institutions

Identifying and fighting insider threats requires the capability to detect anomalous user behavior immediately and accurately. This detection presents its own set of challenges such as appropriately defining what is normal or malicious behavior and setting automated preventive controls to curb predicted threats.

Machine learning-based real-time threat detection for banks

The business impact of the COVID-19 pandemic continues to unfold worldwide for the financial services industry. The “new normal” has not only given rise to unprecedented operational challenges, but also provided fertile ground for hackers and threat actors to take advantage of increased vulnerabilities.

In June 2020, the Internet Crime Complaint Center at the FBI reported a 75% rise in daily digital crime since the start of stay-at-home restrictions. These cyber-crimes are not only becoming more frequent, but also more difficult to detect and more complicated to prevent. Financial institutions like banks that run hundreds of sensitive customer-facing applications are at extremely high risk.