Product Manager, Suitability Surveillance
Anand Maheshwari

The industry’s full of noise about AI, machine learning and analytics. But implementing AI for AI’s sake, without clear goals in mind, does little to improve compliance. The problem isn’t that you need more analytics, but that you need the right analytics to increase AML effectiveness and efficiency. 

Developing and implementing those models can be even harder than determining which analytics to use if you don’t have the right systems in place.  

Join NICE Actimize for this series on demystifying advanced AML analytics. Our experts will explore the different AI and machine learning capabilities available today and how they can enhance your money laundering monitoring and detection.

Discover: 

Can’t watch live? Register now and watch on demand!
Part 1: Your Toolbox: Analytics for KYC and AML programs
On Demand
  • Understand the value and benefits of an end-to-end analytical approach
  • Determine your low hanging fruit–which established AML-KYC analytics will give you the most value? 

Part 2: Entity Resolution  
On Demand
  • See the impact of adding entity resolution to your financial crime solutions
  • Learn how to determine the ROI of entity resolution

Part 3: Advanced Segmentation and Score Scale/Threshold Tuning
On Demand 
  • Identify the benefits of machine learning-driven segmentation and model tuning
  • Discover common pitfalls–Why do most segmentation efforts fail?

Part 4: Model Simulation
On Demand
  • Determine when you should simulate models and why
  • Understand crucial steps when setting up a test and potential errors to avoid
Part 5: Network Analytics
On Demand
  • Discover how network analytics can be used to uncover risk both at the detection and investigation layers
  • Understand how to effectively set up graph networks
Part 6: Anomaly Detection
On Demand
  • Discuss the use cases of anomaly detection
  • Explore the difference between time series and point anomalies
Part 7: Predictive Scoring
On Demand
  • Discuss the benefits of feature engineering in machine learning
  • Explore how historical data can be used to predict likelihood of risk

Register here!