SAS Financial Crimes Analytics
Augment your anti-financial crime program with intelligent and trustworthy AI-powered decisions.
Fight financial crimes with greater effectiveness and efficiency by harnessing the power of AI/machine learning, extensive anti-financial crime domain expertise and global field experience.
Gain a holistic view of financial crime risk.
Financial crime risk dashboards give you a holistic view of your financial crime risk and enhance your risk coverage by visually identifying points of optimization and emerging risks and typologies across large volumes of data.
Dramatically reduce false positives and easily identify anomalies.
Using dynamic segmentation, you can create smart peer groupings to set risk-based thresholds, vastly reduce false positives and easily identify anomalies in dynamically changing patterns and behaviors.
Proactively prevent financial crime activities.
Use predictive analytics and model-based scenarios to detect financial crime threats at an early stage and look into hundreds of financial crime features that are otherwise difficult to maintain using traditional rule-based scenarios. This results in the proactive prevention and reporting of financial crime activities to authorities.
Improve efficiency by focusing on high-risk cases.
Utilize machine learning models to effectively learn from past decisions, intelligently prioritize and expedite the investigation of high-risk cases, and hibernate low-risk events until they are deemed worthy of investigation.
Uncover hidden risks.
Leverage entity resolution, pattern matching and network detection techniques to identify unique entities and uncover hidden risks and relationships to both enhance investigations and alert on complex financial crime patterns and methods.
Get to Know SAS Financial Crimes Analytics
Key Features
Deploy fast, data-backed decisions without the need to rip and replace existing anti-financial crime systems.
Easy data exploration & visualization
Lets you easily import your own data, build and run transformations, and augment and join data within an integrated visual pipeline of activities with a simple drag-and-drop interface.
Quick operationalization of analytical models
Operationalizes models quickly using automated techniques in just a few clicks – in batch and real time. A repeatable framework enables easy registration, validation, tracking, monitoring and retraining of analytical models to ensure they’re performing well.
Seamless integration
Uses advanced data management and powerful in-memory capabilities to integrate with your existing transaction monitoring platform. Works with your existing AML solutions, so there's no need to replace your current platform.
Easy-to-use analytics
Lets you visually explore and evaluate segments for further analysis using k-means clustering, scatter plots and detailed summary statistics. Use advanced machine learning techniques to build and refine predictive models to target specific groups or segments, run numerous what-if scenarios simultaneously, and process results for each group or segment without having to sort or index data each time.
Anti-financial crime optimization
Performs intelligent customer segmentation, entity resolution and scenario threshold tuning with above-the-line/below-the-line testing to generate more productive alerts, identify “true positives” and optimize overall transaction monitoring processes.
Cloud-native, elastic & scalable
Runs on SAS® Viya®, an elastic, scalable platform for public and private clouds. Rapidly processes large data sets and accelerates the complex analytics life cycle, from data preparation to discovery to deployment. The elasticity of compute and data environments supports growth in transaction volumes and complexity of model validation.
Explore More on SAS Financial Crimes Analytics
White Paper
Aite Novarica: AML Modernization. Embarking on the Journey
Uplifting customer experiences and complying with AML regulatory requirements do not line up neatly. This paper explores current organizational challenges, outlines the benefits of new AML technology adoption, and identifies how to embark on a journey of discovery and modernization.
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