Fraud, Anti-Money Laundering & Security Intelligence

Borrowing from agile software development practices, the DataOps phase provides an agile approach to data access, quality, preparation and governance. It enables greater reliability, adaptability, speed and collaboration, using SAS capabilities such as entity resolution, an open data model, data importing and the ability to orchestrate all analytic activities so you can:

• Gain a holistic view of risk.
• Authenticate a customer’s identity.
• Automatically tune models through an adaptive learning feedback loop.

Understanding data and building predictive models requires a combination of techniques – statistics, machine learning, deep learning, natural language processing, computer vision, forecasting, optimization and others. SAS provides rapid AI-powered analytics and automated learning, white box technology, continuous monitoring and surveillance loops, and automated modeling so you can:

• Accurately detect rare events and reduce false positives.
• Rapidly detect emerging threats.
• Reduce time to intelligence.

The ModelOps phase focuses on getting AI models through validation, testing and deployment as quickly as possible, while ensuring quality results. It also focuses on ongoing model monitoring, retraining and governance to ensure peak performance and transparent decisions in day-to-day processes through case management and workflows, a user-friendly interface and intuitive dashboard, a real-time decision engine, and customer-specific rules that:

• Enhance productivity.
• Improve the customer experience.
• Protect your organization’s reputation.