Financial Crime World

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Saudi Arabia Takes a Leap Forward in Financial Crime Data Analytics

Enhancing Fight Against Financial Crime with Cutting-Edge Tools

Riyadh - Saudi Arabia has made significant strides in its fight against financial crime by introducing cutting-edge data analytics tools. This innovation is expected to boost anti-money laundering (AML) efforts across the country, enabling organizations to make informed decisions and stay ahead of financial crime threats.

Key Features of the New System

  • Fast and Informed Decision-Making: The system enables organizations to deploy data-backed strategies without replacing existing AML systems.
  • Easy Data Exploration and Visualization: Businesses can quickly identify areas of risk and develop targeted solutions using advanced analytics capabilities.
  • Operationalizing Analytical Models at Speed: Automated techniques allow for the quick deployment of models, ensuring that they are performing well.

Integration with Existing Systems

The system seamlessly integrates with existing transaction monitoring platforms, eliminating the need for costly replacements. This integration ensures a smooth transition and allows businesses to work efficiently with their current AML solutions.

Advanced Analytics Capabilities

  • Visual Exploration and Evaluation: Users can visually explore and evaluate segments for further analysis using techniques such as k-means clustering and scatter plots.
  • Predictive Modeling: Advanced machine learning techniques enable organizations to build and refine predictive models to target specific groups or segments.

Anti-Financial Crime Optimization Features

The system includes intelligent customer segmentation, entity resolution, and scenario threshold tuning, enabling businesses to generate more productive alerts and identify true positives.

Running on the SAS Viya Platform

The system is built on the SAS Viya platform, a cloud-native, elastic, and scalable solution that can rapidly process large data sets and accelerate the complex analytics life cycle. The elasticity of compute and data environments supports growth in transaction volumes and complexity of model validation.