NICE Actimize Xceed vs Splunk User Behavior Analytics comparison

NICE Actimize Xceed leverages advanced analytics to provide effective fraud and financial crime management, ensuring comprehensive protection for financial institutions. Its modular approach allows tailored configurations suited to specific risk management requirements.

NICE Actimize Xceed offers a comprehensive suite designed for financial institutions aiming to strengthen their financial crime defenses. It integrates seamlessly within existing systems, providing robust tools for monitoring transactions, identifying fraudulent activities, and complying with regulatory standards. The platform supports rapid decision-making with its real-time data analytics, which enhances detection capabilities across numerous financial operations. Its scalable architecture ensures adaptability to varying business needs, making it a reliable choice for risk management professionals seeking precision and efficiency.

• Advanced Analytics: Utilizes AI and machine learning for accurate threat detection.
• Real-Time Monitoring: Continuous monitoring allows immediate threat response.
• Compliance Management: Helps to meet regulatory requirements effortlessly.
• Modular Design: Customizable components to fit specific risk strategies.

• Cost Efficiency: Offers a balance of cost and performance for financial management.
• Operational Efficiency: Enables streamlined operations through automation.
• Scalability: Grows with business needs to ensure long-term value.
• Regulatory Compliance: Keeps up with ever-changing legal standards.

In the banking industry, NICE Actimize Xceed is implemented to detect fraud in online transactions swiftly. In insurance, it aids in identifying false claims efficiently. This adaptability across various sectors demonstrates its capability to mitigate risks while maintaining compliance and operational efficiency.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.