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Having an effective transaction monitoring (TM) system in place is critical for financial institutions to mitigate the AML transaction monitoring risks and to avoid regulatory scrutiny. Constantly evolving regulations and regulatory encouragement to innovate and adopt AI technologies can pose challenges to financial institutions when selecting and implementing a new or upgrading their existing TM system.
When designed incorrectly the system could lead to an excessive amount of false positives resulting in operational inefficiency, inadequate risk coverage and even regulatory fines. Join NICE Actimize and Matrix-IFS in a webinar that will explore the capabilities of a modern transaction monitoring system, as well as best practices for designing and implementing an effective TM system.
Topics covered:
The benefits of Machine Learning & RPA to AML programs
• Introduction to Autonomous AML
• The value of reducing false positives and reducing investigation times
• The power of Machine Learning and RPA
Best practices implementation
• Coverage assessment – identifying AML risks & mitigating controls
• Data identification and sourcing for transaction monitoring
• Model development & configuration based on the organization’s risk
• Customer segmentation & tuning
• Testing
• Regulatory documentation
• Ongoing model performance monitoring
audience
NICE Actimize customers and enthusiasts
date & time
location
Having an effective transaction monitoring (TM) system in place is critical for financial institutions to mitigate the AML transaction monitoring risks and to avoid regulatory scrutiny. Constantly evolving regulations and regulatory encouragement to innovate and adopt AI technologies can pose challenges to financial institutions when selecting and implementing a new or upgrading their existing TM system.
When designed incorrectly the system could lead to an excessive amount of false positives resulting in operational inefficiency, inadequate risk coverage and even regulatory fines. Join NICE Actimize and Matrix-IFS in a webinar that will explore the capabilities of a modern transaction monitoring system, as well as best practices for designing and implementing an effective TM system.
Topics covered:
The benefits of Machine Learning & RPA to AML programs
• Introduction to Autonomous AML
• The value of reducing false positives and reducing investigation times
• The power of Machine Learning and RPA
Best practices implementation
• Coverage assessment – identifying AML risks & mitigating controls
• Data identification and sourcing for transaction monitoring
• Model development & configuration based on the organization’s risk
• Customer segmentation & tuning
• Testing
• Regulatory documentation
• Ongoing model performance monitoring
